Method and system for using artificial intelligence and machine learning to create optimal treatment plans based on monetary value amount generated and/or patient outcome

ABSTRACT

A method is disclosed for generating treatment plans for optimizing patient outcome and monetary value amount generated. The method includes receiving a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients, receiving a set of monetary value amounts associated with the set of treatment plans, receiving a set of constraints, where the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans. The method includes generating, by the artificial intelligence engine, optimal treatment plans for a patient, where the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints. Each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated. The method includes transmitting the optimal treatment plans to a computing device.

BACKGROUND

Remote medical assistance, or telemedicine, may aid a patient inperforming various aspects of a rehabilitation regimen for a body part.The patient may use a patient interface in communication with anassistant interface for receiving the remote medical assistance viaaudio and/or audiovisual communications.

SUMMARY

In one embodiment, a method is disclosed for generating treatment plansfor optimizing patient outcome and monetary value amount generated. Themethod includes receiving a set of treatment plans that, when applied topatients, cause outcomes to be achieved by the patients, receiving a setof monetary value amounts associated with the set of treatment plans,receiving a set of constraints, where the set of constraints comprisesrules pertaining to billing codes associated with the set of treatmentplans. The method includes generating, by the artificial intelligenceengine, optimal treatment plans for a patient, where the generating isbased on the set of treatment plans, the set of monetary value amounts,and the set of constraints. Each of the optimal treatment plans complieswith the set of constraints and represents a patient outcome and anassociated monetary value amount generated. The method includestransmitting the optimal treatment plans to a computing device.

In one embodiment, a system includes a memory storing instructions and aprocessing device communicatively coupled to the memory. The processingdevice executes the instructions to perform any of the methods,operations, or steps described herein.

In one embodiment, a tangible, non-transitory computer-readable mediumstores instructions that, when executed, cause a processing device toperform any of the methods, operations, or steps described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of example embodiments, reference will now bemade to the accompanying drawings in which:

FIG. 1 shows a block diagram of an embodiment of a computer implementedsystem for managing a treatment plan according to the presentdisclosure;

FIG. 2 shows a perspective view of an embodiment of a treatmentapparatus according to the present disclosure;

FIG. 3 shows a perspective view of a pedal of the treatment apparatus ofFIG. 2 according to the present disclosure;

FIG. 4 shows a perspective view of a person using the treatmentapparatus of FIG. 2 according to the present disclosure;

FIG. 5 shows an example embodiment of an overview display of anassistant interface according to the present disclosure;

FIG. 6 shows an example block diagram of training a machine learningmodel to output, based on data pertaining to the patient, a treatmentplan for the patient according to the present disclosure;

FIG. 7 shows an embodiment of an overview display of the assistantinterface presenting recommended treatment plans and excluded treatmentplans in real-time during a telemedicine session according to thepresent disclosure;

FIG. 8 shows an embodiment of the overview display of the assistantinterface presenting, in real-time during a telemedicine session,recommended treatment plans that have changed as a result of patientdata changing according to the present disclosure;

FIG. 9 shows an embodiment of the overview display of the assistantinterface presenting, in real-time during a telemedicine session,treatment plans and billing sequences tailored for certain parametersaccording to the present disclosure;

FIG. 10 shows an example embodiment of a method for generating, based ona set of billing procedures, a billing sequence tailored for aparticular parameter, where the billing sequence pertains to a treatmentplan according to the present disclosure;

FIG. 11 shows an example embodiment of a method for receiving requestsfrom computing devices and modifying the billing sequence based on therequests according to the present disclosure;

FIG. 12 shows an embodiment of the overview display of the assistantinterface presenting, in real-time during a telemedicine session,optimal treatment plans that generate certain monetary value amounts andresult in certain patient outcomes according to the present disclosure;

FIG. 13 shows an example embodiment of a method for generating optimaltreatment plans for a patient, where the generating is based on a set oftreatment plans, a set of money value amounts, and a set of constraintsaccording to the present disclosure;

FIG. 14 shows an example embodiment of a method for receiving aselection of a monetary value amount and generating an optimal treatmentplan based on a set of treatment plans, the monetary value amount, and aset of constraints according to the present disclosure;

FIG. 15 shows an example embodiment of a method for receiving aselection of an optimal treatment plan and controlling, based on theoptimal treatment plan, a treatment apparatus while the patient uses thetreatment apparatus according to the present disclosure; and

FIG. 16 shows an example computer system according to the presentdisclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different companies may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . .” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer, or section from another region,layer, or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer, or section discussed below could be termed a second element,component, region, layer, or section without departing from theteachings of the example embodiments. The phrase “at least one of,” whenused with a list of items, means that different combinations of one ormore of the listed items may be used, and only one item in the list maybe needed. For example, “at least one of: A, B, and C” includes any ofthe following combinations: A, B, C, A and B, A and C, B and C, and Aand B and C. In another example, the phrase “one or more” when used witha list of items means there may be one item or any suitable number ofitems exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” “top,” “bottom,” and the like, may be usedherein. These spatially relative terms can be used for ease ofdescription to describe one element's or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms may also be intended to encompass differentorientations of the device in use, or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, the example term “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptions used herein interpreted accordingly.

A “treatment plan” may include one or more treatment protocols, and eachtreatment protocol includes one or more treatment sessions. Eachtreatment session comprises several session periods, with each sessionperiod including a particular exercise for treating the body part of thepatient. For example, a treatment plan for post-operative rehabilitationafter a knee surgery may include an initial treatment protocol withtwice daily stretching sessions for the first 3 days after surgery and amore intensive treatment protocol with active exercise sessionsperformed 4 times per day starting 4 days after surgery. A treatmentplan may also include information pertaining to a medical procedure toperform on the patient, a treatment protocol for the patient using atreatment apparatus, a diet regimen for the patient, a medicationregimen for the patient, a sleep regimen for the patient, additionalregimens, or some combination thereof.

The terms telemedicine, telehealth, telemed, teletherapeutic, etc. maybe used interchangeably herein.

The term “monetary value amount” (singular or plural) may refer to fees,revenue, profit (e.g., gross, net, etc.), earnings before interest(EBIT), earnings before interest, depreciation and amortization(EBITDA), cash flow, free cash flow, working capital, gross revenue, avalue of warrants, options, equity, debt, derivatives or any otherfinancial instrument, any generally acceptable financial measure ormetric in corporate finance or according to Generally AcceptedAccounting Principles (GAAP) or foreign counterparts, or the like.

The term “optimal treatment plan” may refer to optimizing a treatmentplan based on a certain parameter or combinations of more than oneparameter, such as, but not limited to, a monetary value amountgenerated by a treatment plan and/or billing sequence, wherein themonetary value amount is measured by an absolute amount in dollars oranother currency, a Net Present Value (NPV) or any other measure, apatient outcome that results from the treatment plan and/or billingsequence, a fee paid to a medical professional, a payment plan for thepatient to pay off an amount of money owed or a portion thereof, a planof reimbursement, an amount of revenue, profit or other monetary valueamount to be paid to an insurance or third-party provider, or somecombination thereof.

The term billing sequence may refer to an order in which billing codesassociated with procedures or instructions of a treatment plan arebilled.

The term billing codes may refer any suitable type of medical coding,such as Current Procedural Terminology (CPT), Diagnosis Related Groups(DRGs), International Classification of Disease, Tenth Edition (ICD-10),and Healthcare Common Procedural Coding System (HCPCS).

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

Determining a treatment plan for a patient having certaincharacteristics (e.g., vital-sign or other measurements; performance;demographic; geographic; diagnostic; measurement- or test-based;medically historic; etiologic; cohort-associative; differentiallydiagnostic; surgical, physically therapeutic, pharmacologic and othertreatment(s) recommended; etc.) may be a technically challengingproblem. For example, a multitude of information may be considered whendetermining a treatment plan, which may result in inefficiencies andinaccuracies in the treatment plan selection process. In arehabilitative setting, some of the multitude of information consideredmay include characteristics of the patient such as personal information,performance information, and measurement information. The personalinformation may include, e.g., demographic, psychographic or otherinformation, such as an age, a weight, a gender, a height, a body massindex, a medical condition, a familial medication history, an injury, amedical procedure, a medication prescribed, or some combination thereof.The performance information may include, e.g., an elapsed time of usinga treatment apparatus, an amount of force exerted on a portion of thetreatment apparatus, a range of motion achieved on the treatmentapparatus, a movement speed of a portion of the treatment apparatus, anindication of a plurality of pain levels using the treatment apparatus,or some combination thereof. The measurement information may include,e.g., a vital sign, a respiration rate, a heartrate, a temperature, ablood pressure, or some combination thereof. It may be desirable toprocess the characteristics of a multitude of patients, the treatmentplans performed for those patients, and the results of the treatmentplans for those patients.

Further, another technical problem may involve distally treating, via acomputing device during a telemedicine or telehealth session, a patientfrom a location different than a location at which the patient islocated. An additional technical problem is controlling or enabling thecontrol of, from the different location, a treatment apparatus used bythe patient at the location at which the patient is located. Oftentimes,when a patient undergoes rehabilitative surgery (e.g., knee surgery), aphysical therapist or other medical professional may prescribe atreatment apparatus to the patient to use to perform a treatmentprotocol at their residence or any mobile location or temporarydomicile. A medical professional may refer to a doctor, physicianassistant, nurse, chiropractor, dentist, physical therapist,acupuncturist, physical trainer, or the like. A medical professional mayrefer to any person with a credential, license, degree, or the like inthe field of medicine, physical therapy, rehabilitation, or the like.

Since the physical therapist or other medical professional is located ina different location from the patient and the treatment apparatus, itmay be technically challenging for the physical therapist or othermedical professional to monitor the patient's actual progress (asopposed to relying on the patient's word about their progress) using thetreatment apparatus, modify the treatment plan according to thepatient's progress, adapt the treatment apparatus to the personalcharacteristics of the patient as the patient performs the treatmentplan, and the like.

Accordingly, some embodiments of the present disclosure pertain to usingartificial intelligence and/or machine learning to assign patients tocohorts and to dynamically control a treatment apparatus based on theassignment during an adaptive telemedical session. In some embodiments,numerous treatment apparatuses may be provided to patients. Thetreatment apparatuses may be used by the patients to perform treatmentplans in their residences, at a gym, at a rehabilitative center, at ahospital, or any suitable location, including permanent or temporarydomiciles. In some embodiments, the treatment apparatuses may becommunicatively coupled to a server. Characteristics of the patients maybe collected before, during, and/or after the patients perform thetreatment plans. For example, the personal information, the performanceinformation, and the measurement information may be collected before,during, and/or after the person performs the treatment plans. Theresults (e.g., improved performance or decreased performance) ofperforming each exercise may be collected from the treatment apparatusthroughout the treatment plan and after the treatment plan is performed.The parameters, settings, configurations, etc. (e.g., position of pedal,amount of resistance, etc.) of the treatment apparatus may be collectedbefore, during, and/or after the treatment plan is performed.

Each characteristic of the patient, each result, and each parameter,setting, configuration, etc. may be timestamped and may be correlatedwith a particular step in the treatment plan. Such a technique mayenable determining which steps in the treatment plan lead to desiredresults (e.g., improved muscle strength, range of motion, etc.) andwhich steps lead to diminishing returns (e.g., continuing to exerciseafter 3 minutes actually delays or harms recovery).

Data may be collected from the treatment apparatuses and/or any suitablecomputing device (e.g., computing devices where personal information isentered, such as a clinician interface or patient interface) over timeas the patients use the treatment apparatuses to perform the varioustreatment plans. The data that may be collected may include thecharacteristics of the patients, the treatment plans performed by thepatients, and the results of the treatment plans.

In some embodiments, the data may be processed to group certain peopleinto cohorts. The people may be grouped by people having certain orselected similar characteristics, treatment plans, and results ofperforming the treatment plans. For example, athletic people having nomedical conditions who perform a treatment plan (e.g., use the treatmentapparatus for 30 minutes a day 5 times a week for 3 weeks) and who fullyrecover may be grouped into a first cohort. Older people who areclassified obese and who perform a treatment plan (e.g., use thetreatment plan for 10 minutes a day 3 times a week for 4 weeks) and whoimprove their range of motion by 75 percent may be grouped into a secondcohort.

In some embodiments, an artificial intelligence engine may include oneor more machine learning models that are trained using the cohorts. Forexample, the one or more machine learning models may be trained toreceive an input of characteristics of a new patient and to output atreatment plan for the patient that results in a desired result. Themachine learning models may match a pattern between the characteristicsof the new patient and at least one patient of the patients included ina particular cohort. When a pattern is matched, the machine learningmodels may assign the new patient to the particular cohort and selectthe treatment plan associated with the at least one patient. Theartificial intelligence engine may be configured to control, distallyand based on the treatment plan, the treatment apparatus while the newpatient uses the treatment apparatus to perform the treatment plan.

As may be appreciated, the characteristics of the new patient may changeas the new patient uses the treatment apparatus to perform the treatmentplan. For example, the performance of the patient may improve quickerthan expected for people in the cohort to which the new patient iscurrently assigned. Accordingly, the machine learning models may betrained to dynamically reassign, based on the changed characteristics,the new patient to a different cohort that includes people havingcharacteristics similar to the now-changed characteristics as the newpatient. For example, a clinically obese patient may lose weight and nolonger meet the weight criterion for the initial cohort, result in thepatient's being reassigned to a different cohort with a different weightcriterion. A different treatment plan may be selected for the newpatient, and the treatment apparatus may be controlled, distally andbased on the different treatment plan, the treatment apparatus while thenew patient uses the treatment apparatus to perform the treatment plan.Such techniques may provide the technical solution of distallycontrolling a treatment apparatus. Further, the techniques may lead tofaster recovery times and/or better results for the patients because thetreatment plan that most accurately fits their characteristics isselected and implemented, in real-time, at any given moment. Real-timemay refer to less than or equal to 2 seconds. Near real-time may referto any interaction of a sufficiently short time to enable twoindividuals to engage in a dialogue via such user interface, and willgenerally be less than 10 seconds but greater than 2 seconds. Asdescribed herein, the term “results” may refer to medical results ormedical outcomes. Results and outcomes may refer to responses to medicalactions.

Depending on what result is desired, the artificial intelligence enginemay be trained to output several treatment plans. For example, oneresult may include recovering to a threshold level (e.g., 75% range ofmotion) in a fastest amount of time, while another result may includefully recovering (e.g., 100% range of motion) regardless of the amountof time. The data obtained from the patients and sorted into cohorts mayindicate that a first treatment plan provides the first result forpeople with characteristics similar to the patient's, and that a secondtreatment plan provides the second result for people withcharacteristics similar to the patient.

Further, the artificial intelligence engine may also be trained tooutput treatment plans that are not optimal or sub-optimal or eveninappropriate (all referred to, without limitation, as “excludedtreatment plans”) for the patient. For example, if a patient has highblood pressure, a particular exercise may not be approved or suitablefor the patient as it may put the patient at unnecessary risk or eveninduce a hypertensive crisis and, accordingly, that exercise may beflagged in the excluded treatment plan for the patient.

In some embodiments, the treatment plans and/or excluded treatment plansmay be presented, during a telemedicine or telehealth session, to amedical professional. The medical professional may select a particulartreatment plan for the patient to cause that treatment plan to betransmitted to the patient and/or to control, based on the treatmentplan, the treatment apparatus. In some embodiments, to facilitatetelehealth or telemedicine applications, including remote diagnoses,determination of treatment plans and rehabilitative and/or pharmacologicprescriptions, the artificial intelligence engine may receive and/oroperate distally from the patient and the treatment apparatus. In suchcases, the recommended treatment plans and/or excluded treatment plansmay be presented simultaneously with a video of the patient in real-timeor near real-time during a telemedicine or telehealth session on a userinterface of a computing device of a medical professional. The video mayalso be accompanied by audio, text and other multimedia information.Real-time may refer to less than or equal to 2 seconds. Near real-timemay refer to any interaction of a sufficiently short time to enable twoindividuals to engage in a dialogue via such user interface, and willgenerally be less than 10 seconds but greater than 2 seconds.

Presenting the treatment plans generated by the artificial intelligenceengine concurrently with a presentation of the patient video may providean enhanced user interface because the medical professional may continueto visually and/or otherwise communicate with the patient while alsoreviewing the treatment plans on the same user interface. The enhanceduser interface may improve the medical professional's experience usingthe computing device and may encourage the medical professional to reusethe user interface. Such a technique may also reduce computing resources(e.g., processing, memory, network) because the medical professionaldoes not have to switch to another user interface screen to enter aquery for a treatment plan to recommend based on the characteristics ofthe patient. The artificial intelligence engine provides, dynamically onthe fly, the treatment plans and excluded treatment plans.

Additionally, some embodiments of the present disclosure may relate toanalytically optimizing telehealth practice-based billing processes andrevenue while enabling regulatory compliance. Information of a patient'scondition may be received and the information may be used to determinethe procedures (e.g., the procedures may include one or more officevisits, bloodwork tests, other medical tests, surgeries, biopsies,performances of exercise or exercises, therapy sessions, physicaltherapy sessions, lab studies, consultations, or the like) to perform onthe patient. Based on the information, a treatment plan may be generatedfor the patient. The treatment plan may include various instructionspertaining at least to the procedures to perform for the patient'scondition. There may be an optimal way to bill the procedures and costsassociated with the billing. However, there may be a set of billingprocedures associated with the set of instructions. The set of billingprocedures may include a set of rules pertaining to billing codes,timing, constraints, or some combination thereof that govern the orderin which the procedures are allowed to be billed and, further, whichprocedures are allowed to be billed or which portions of a givenprocedure are allowed to be billed. For example, regarding timing, atest may be allowed to be conducted before surgery but not after thesurgery. In his example, it may be best for the patient to conduct thetest before the surgery. Accordingly, the billing sequence may include abilling code for the test before a billing code for the surgery. Theconstraints may pertain to an insurance regime, a medical order, laws,regulations, or the like. Regarding the order, an example may include:if procedure A is performed, then procedure B may be billed, butprocedure A cannot be billed if procedure B was billed first. It may notbe a trivial task to optimize a billing sequence for a treatment planwhile complying with the set of rules.

It is desirable to generate a billing sequence for the patient'streatment plan that complies with the set of rules. In addition, thereare multiples of parameters to consider for a desired billing sequence.The parameters may pertain to a monetary value amount generated by thebilling sequence, a patient outcome that results from the treatment planassociated with the billing sequence, a fee paid to a medicalprofessional, a payment plan for the patient to pay off an amount ofmoney owed, a plan of reimbursement, an amount of revenue to be paid toan insurance provider, or some combination thereof.

The artificial intelligence engine may be trained to generate, based onthe set of billing procedures, one or more billing sequences for atleast a portion of or all of the instructions, where the billingsequence is tailored according to one or more of the parameters. Assuch, the disclosed techniques may enable medical professionals toprovide, improve or come closer to achieving best practices for ethicalpatient care. By complying with the set of billing procedures, thedisclosed techniques provide for ethical consideration of the patient'scare, while also benefiting the practice of the medical professional andbenefiting the interests of insurance providers. In other words, one keygoal of the disclosed techniques is to maximize both patient carequality and the degree of reimbursement for the use of ethical medicalpractices related thereto.

The artificial intelligence engine may pattern match to generate billingsequences and/or treatment plans tailored for a selected parameter(e.g., best outcome for the patient, maximize monetary value amountgenerated, etc.). Different machine learning models may be trained togenerate billing sequences and/or treatment plans for differentparameters. In some embodiments, one trained machine learning model thatgenerates a first billing sequence for a first parameter (e.g., monetaryvalue amount generated) may be linked to and feed its output to anothertrained machine learning model that generates a second billing sequencefor a second parameter (e.g., a plan of reimbursement). Thus, the secondbilling sequence may be tuned for both the first parameter and thesecond parameter. It should be understood that any suitable combinationof trained machine learning models may be used to provide billingsequences and/or treatment plans tailored to any combination of theparameters described herein, as well as other parameters contemplatedand/or used in billing sequences and/or treatment plans, whether or notspecifically expressed or enumerated herein.

In some embodiments, a medical professional and an insurance company mayparticipate to provide requests pertaining to the billing sequence. Forexample, the medical professional and the insurance company may requestto receive immediate reimbursement for the treatment plan. Accordingly,the artificial intelligence engine may be trained to generate, based onthe immediate reimbursement requests, a modified billing sequence thatcomplies with the set of billing procedures and provides for immediatereimbursement to the medical professional and the insurance company.

In some embodiments, the treatment plan may be modified by a medicalprofessional. For example, certain procedures may be added, modified orremoved. In the telehealth scenario, there are certain procedures thatmay not be performed due to the distal nature of a medical professionalusing a computing device in a different physical location than apatient.

In some embodiments, the treatment plan and the billing sequence may betransmitted to a computing device of a medical professional, insuranceprovider, any lawfully designated or appointed entity and/or patient. Itshould be noted that there may be other entities that receive thetreatment plan and the billing sequence for the insurance providerand/or the patient. Such entities may include any lawfully designated orappointed entity (e.g., assignees, legally predicated designees,attorneys-in-fact, legal proxies, etc.), Thus, as used herein, it shouldbe understood that these entities may receive information in lieu of, inaddition to the insurance provider and/or the patient, or as anintermediary or interlocutor between another such lawfully designated orappointed entity and the insurance provider and/or the patient. Thetreatment plan and the billing sequence may be presented in a firstportion of a user interface on the computing device. A video of thepatient or the medical professional may be optionally presented in asecond portion of the user interface on the computing device. The firstportion (including the treatment plan and the billing sequence) and thesecond portion (including the video) may be presented concurrently onthe user interface to enable to the medical professional and/or thepatient to view the video and the treatment plan and the billingsequence at the same time. Such a technique may be beneficial and reducecomputing resources because the user (medical professional and/orpatient) does not have to minimize the user interface (including thevideo) in order to open another user interface which includes thetreatment plan and the billing sequence.

In some embodiments, the medical professional and/or the patient mayselect a certain treatment plan and/or billing sequence from the userinterface. Based on the selection, the treatment apparatus may beelectronically controlled, either via the computing device of thepatient transmitting a control signal to a controller of the treatmentapparatus, or via the computing device of the medical professionaltransmitting a control signal to the controller of the treatmentapparatus. As such, the treatment apparatus may initialize the treatmentplan and configure various settings (e.g., position of pedals, speed ofpedaling, amount of force required on pedals, etc.) defined by thetreatment plan.

A potential technical problem may relate to the information pertainingto the patient's medical condition being received in disparate formats.For example, a server may receive the information pertaining to amedical condition of the patient from one or more sources (e.g., from anelectronic medical record (EMR) system, application programminginterface (API), or any suitable system that has information pertainingto the medical condition of the patient). That is, some sources used byvarious medical professional entities may be installed on their localcomputing devices and, additionally and/or alternatively, may useproprietary formats. Accordingly, some embodiments of the presentdisclosure may use an API to obtain, via interfaces exposed by APIs usedby the sources, the formats used by the sources. In some embodiments,when information is received from the sources, the API may map andconvert the format used by the sources to a standardized (i.e.,canonical) format, language and/or encoding (“format” as used hereinwill be inclusive of all of these terms) used by the artificialintelligence engine. Further, the information converted to thestandardized format used by the artificial intelligence engine may bestored in a database accessed by the artificial intelligence engine whenthe artificial intelligence engine is performing any of the techniquesdisclosed herein. Using the information converted to a standardizedformat may enable a more accurate determination of the procedures toperform for the patient and/or a billing sequence to use for thepatient.

To that end, the standardized information may enable generatingtreatment plans and/or billing sequences having a particular format thatcan be processed by various applications (e.g., telehealth). Forexample, applications, such as telehealth applications, may be executingon various computing devices of medical professionals and/or patients.The applications (e.g., standalone or web-based) may be provided by aserver and may be configured to process data according to a format inwhich the treatment plans and the billing sequences are implemented.Accordingly, the disclosed embodiments may provide a technical solutionby (i) receiving, from various sources (e.g., EMR systems), informationin non-standardized and/or different formats; (ii) standardizing theinformation (i.e., representing the information in a canonical format);and (iii) generating, based on the standardized information, treatmentplans and billing sequences having standardized formats capable of beingprocessed by applications (e.g., telehealth applications) executing oncomputing devices of medical professionals and/or patients and/or theirlawfully authorized designees.

Additionally, some embodiments of the present disclosure may useartificial intelligence and machine learning to create optimal patienttreatment plans based on one or more of monetary value amount andpatient outcomes. Optimizing for one or more of patient outcome andmonetary value amount generated, while complying with a set ofconstraints, may be a computationally and technically challenging issue.

Accordingly, the disclosed techniques provide numerous technicalsolutions in embodiments that enable dynamically determining one or moreoptimal treatment plans optimized for various parameters (e.g., monetaryvalue amount generated, patient outcome, risk, etc.). In someembodiments, while complying with the set of constraints, an artificialintelligence engine may use one or more trained machine learning modelsto generate the optimal treatment plans for various parameters. The setof constraints may pertain to billing codes associated with varioustreatment plans, laws, regulations, timings of billing, orders ofbilling, and the like. As described herein, one or more of the optimaltreatment plans may be selected to control, based on the selected one ormore treatment plans, the treatment apparatus in real-time or nearreal-time while a patient uses the treatment apparatus in a telehealthor telemedicine session.

One of the parameters may include maximizing an amount of monetary valueamount generated. Accordingly, in one embodiment, the artificialintelligence engine may receive information pertaining to a medicalcondition of the patient. Based on the information, the artificialintelligence engine may receive a set of treatment plans that, whenapplied to other patients having similar medical condition information,cause outcomes to be achieved by the patients. The artificialintelligence engine may receive a set of monetary value amountsassociated with the set of treatment plans. A respective monetary valueamount may be associated with a respective treatment plan. Theartificial intelligence engine may receive the set of constraints. Theartificial intelligence engine may generate optimal treatment plans fora patient, where the generating is based on one or more of the set oftreatment plans, the set of monetary value amounts, and the set ofconstraints. Each of the optimal treatment plans complies completely orto the maximum extent possible or to a prescribed extent with the set ofconstraints and represents a patient outcome and an associated monetaryvalue amount generated. The optimal treatment plans may be transmitted,in real-time or near real-time, during a telehealth or telemedicinesession, to be presented on one or more computing devices of one or moremedical professionals and/or one or more patients. It should be notedthat the term “telehealth” as used herein will be inclusive of all ofthe following terms: telemedicine, teletherapeutic, telerehab, etc. Itshould be noted that the term “telemedicine” as used herein will beinclusive of all of the following terms: telehealth, teletherapeutic,telerehab, etc.

A user may select different monetary value amounts, and the artificialintelligence engine may generate different optimal treatment plans forthose monetary value amounts. The different optimal treatment plans mayrepresent different patient outcomes and may also comply with the set ofconstraints. The different optimal treatment plans may be transmitted,in real-time or near real-time, during a telehealth or telemedicinesession, to be presented on a computing device of a medical professionaland/or a patient.

The disclosed techniques may use one or more equations having certainparameters on a left side of the equation and certain parameters on aright side of the equation. For example, the parameters on the left sideof the equation may represent a treatment plan, patient outcome, risk,and/or monetary value amount generated. The parameters on the right sideof the equation may represent the set of constraints that must becomplied with to ethically and/or legally bill for the treatment plan.Such an equation or equations and/or one or more parameters therein mayalso, without limitation, incorporate or implement appropriatemathematical, statistical and/or probabilistic algorithms as well as usecomputer-based subroutines, methods, operations, function calls,scripts, services, applications or programs to receive certain valuesand to return other values and/or results. The various parameters may beconsidered levers that may be adjusted to provide a desired treatmentplan and/or monetary value amount generated. In some instances, it maybe desirable to select an optimal treatment plan that is tailored for adesired patient outcome (e.g., best recovery, fastest recovery rate,etc.), which may effect the monetary value amount generated and the riskassociated with the treatment plan. In other instances, it may bedesirable to select an optimal treatment plan tailored for a desiredmonetary value amount generated, which may effect the treatment planand/or the risk associated with the treatment plan.

For example, a first treatment plan may result in a first patientoutcome having a low risk and resulting in a low monetary value amountgenerated, whereas a second treatment plan may result in a secondpatient outcome (better than the first patient outcome) having a higherrisk and resulting in a higher monetary value amount generated than thefirst treatment plan. Both the first treatment plan and the secondtreatment plan are generated based on the set of constraints. Also, boththe first treatment plan and the second treatment plan may besimultaneously presented, in real-time or near real-time, on a userinterface of one or more computing devices engaged in a telehealth ortelemedicine session. A user (e.g., medical professional or patient) mayselect either the first or second treatment plan to cause the selectedtreatment plan to be implemented on the treatment apparatus. In otherwords, the treatment apparatus may be electronically controlled based onthe selected treatment plan.

Accordingly, the artificial intelligence engine may use various machinelearning models, each trained to generate one or more optimal treatmentplans for a different parameter, as described further below. Each of theone or more optimal treatment plans complies with the set ofconstraints.

The various embodiments disclosed herein may provide a technicalsolution to the technical problem pertaining to the patient's medicalcondition information being received in disparate formats. For example,a server may receive the information pertaining to a medical conditionof the patient from one or more sources (e.g., from an electronicmedical record (EMR) system, application programming interface (API), orany suitable system that has information pertaining to the medicalcondition of the patient). The information may be converted from theformat used by the sources to the standardized format used by theartificial intelligence engine. Further, the information converted tothe standardized format used by the artificial intelligence engine maybe stored in a database accessed by the artificial intelligence enginewhen performing any of the techniques disclosed herein. The standardizedinformation may enable generating optimal treatment plans, where thegenerating is based on treatment plans associated with the standardizedinformation, monetary value amounts, and the set of constraints. Theoptimal treatment plans may be provided in a standardized format thatcan be processed by various applications (e.g., telehealth) executing onvarious computing devices of medical professionals and/or patients.

In some embodiments, the treatment apparatus may be adaptive and/orpersonalized because its properties, configurations, and positions maybe adapted to the needs of a particular patient. For example, the pedalsmay be dynamically adjusted on the fly (e.g., via a telemedicine sessionor based on programmed configurations in response to certainmeasurements being detected) to increase or decrease a range of motionto comply with a treatment plan designed for the user. In someembodiments, a medical professional may adapt, remotely during atelemedicine session, the treatment apparatus to the needs of thepatient by causing a control instruction to be transmitted from a serverto treatment apparatus. Such adaptive nature may improve the results ofrecovery for a patient, furthering the goals of personalized medicine,and enabling personalization of the treatment plan on a per-individualbasis.

FIG. 1 shows a block diagram of a computer-implemented system 10,hereinafter called “the system” for managing a treatment plan. Managingthe treatment plan may include using an artificial intelligence engineto recommend treatment plans and/or provide excluded treatment plansthat should not be recommended to a patient.

The system 10 also includes a server 30 configured to store and toprovide data related to managing the treatment plan. The server 30 mayinclude one or more computers and may take the form of a distributedand/or virtualized computer or computers. The server 30 also includes afirst communication interface 32 configured to communicate with theclinician interface 20 via a first network 34. In some embodiments, thefirst network 34 may include wired and/or wireless network connectionssuch as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC),cellular data network, etc. The server 30 includes a first processor 36and a first machine-readable storage memory 38, which may be called a“memory” for short, holding first instructions 40 for performing thevarious actions of the server 30 for execution by the first processor36. The server 30 is configured to store data regarding the treatmentplan. For example, the memory 38 includes a system data store 42configured to hold system data, such as data pertaining to treatmentplans for treating one or more patients.

The system data store 42 may be configured to hold data relating tobilling procedures, including rules and constraints pertaining tobilling codes, order, timing, insurance regimes, laws, regulations, orsome combination thereof. The system data store 42 may be configured tostore various billing sequences generated based on billing proceduresand various parameters (e.g., monetary value amount generated, patientoutcome, plan of reimbursement, fees, a payment plan for patients to payof an amount of money owed, an amount of revenue to be paid to aninsurance provider, etc.). The system data store 42 may be configured tostore optimal treatment plans generated based on various treatment plansfor users having similar medical conditions, monetary value amountsgenerated by the treatment plans, and the constraints. Any of the datastored in the system data store 42 may be accessed by an artificialintelligence engine 11 when performing any of the techniques describedherein.

The server 30 is also configured to store data regarding performance bya patient in following a treatment plan. For example, the memory 38includes a patient data store 44 configured to hold patient data, suchas data pertaining to the one or more patients, including datarepresenting each patient's performance within the treatment plan.

In addition, the characteristics (e.g., personal, performance,measurement, etc.) of the people, the treatment plans followed by thepeople, the level of compliance with the treatment plans, and theresults of the treatment plans may use correlations and otherstatistical or probabilistic measures to enable the partitioning of orto partition the treatment plans into different patientcohort-equivalent databases in the patient data store 44. For example,the data for a first cohort of first patients having a first similarinjury, a first similar medical condition, a first similar medicalprocedure performed, a first treatment plan followed by the firstpatient, and a first result of the treatment plan may be stored in afirst patient database. The data for a second cohort of second patientshaving a second similar injury, a second similar medical condition, asecond similar medical procedure performed, a second treatment planfollowed by the second patient, and a second result of the treatmentplan may be stored in a second patient database. Any singlecharacteristic or any combination of characteristics may be used toseparate the cohorts of patients. In some embodiments, the differentcohorts of patients may be stored in different partitions or volumes ofthe same database. There is no specific limit to the number of differentcohorts of patients allowed, other than as limited by mathematicalcombinatoric and/or partition theory.

This characteristic data, treatment plan data, and results data may beobtained from numerous treatment apparatuses and/or computing devicesover time and stored in the database 44. The characteristic data,treatment plan data, and results data may be correlated in thepatient-cohort databases in the patient data store 44. Thecharacteristics of the people may include personal information,performance information, and/or measurement information.

In addition to the historical information about other people stored inthe patient cohort-equivalent databases, real-time or near-real-timeinformation based on the current patient's characteristics about acurrent patient being treated may be stored in an appropriate patientcohort-equivalent database. The characteristics of the patient may bedetermined to match or be similar to the characteristics of anotherperson in a particular cohort (e.g., cohort A) and the patient may beassigned to that cohort.

In some embodiments, the server 30 may execute the artificialintelligence (AI) engine 11 that uses one or more machine learningmodels 13 to perform at least one of the embodiments disclosed herein.The server 30 may include a training engine 9 capable of generating theone or more machine learning models 13. The machine learning models 13may be trained to assign people to certain cohorts based on theircharacteristics, select treatment plans using real-time and historicaldata correlations involving patient cohort-equivalents, and control atreatment apparatus 70, among other things. The machine learning models13 may be trained to generate, based on billing procedures, billingsequences and/or treatment plans tailored for various parameters (e.g.,a fee to be paid to a medical professional, a payment plan for thepatient to pay off an amount of money owed, a plan of reimbursement, anamount of revenue to be paid to an insurance provider, or somecombination thereof). The machine learning models 13 may be trained togenerate, based on constraints, optimal treatment plans tailored forvarious parameters (e.g., monetary value amount generated, patientoutcome, risk, etc.). The one or more machine learning models 13 may begenerated by the training engine 9 and may be implemented in computerinstructions executable by one or more processing devices of thetraining engine 9 and/or the servers 30. To generate the one or moremachine learning models 13, the training engine 9 may train the one ormore machine learning models 13. The one or more machine learning models13 may be used by the artificial intelligence engine 11.

The training engine 9 may be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a smartphone, a laptopcomputer, a tablet computer, a netbook, a desktop computer, an Internetof Things (IoT) device, any other desired computing device, or anycombination of the above. The training engine 9 may be cloud-based or areal-time software platform, and it may include privacy software orprotocols, and/or security software or protocols.

To train the one or more machine learning models 13, the training engine9 may use a training data set of a corpus of the information (e.g.,characteristics, medical diagnosis codes, etc.) pertaining to medicalconditions of the people who used the treatment apparatus 70 to performtreatment plans, the details (e.g., treatment protocol includingexercises, amount of time to perform the exercises, instructions for thepatient to follow, how often to perform the exercises, a schedule ofexercises, parameters/configurations/settings of the treatment apparatus70 throughout each step of the treatment plan, etc.) of the treatmentplans performed by the people using the treatment apparatus 70, theresults of the treatment plans performed by the people, a set ofmonetary value amounts associated with the treatment plans, a set ofconstraints (e.g., rules pertaining to billing codes associated with theset of treatment plans, laws, regulations, etc.), a set of billingprocedures (e.g., rules pertaining to billing codes, order, timing andconstraints) associated with treatment plan instructions, a set ofparameters (e.g., a fee to be paid to a medical professional, a paymentplan for the patient to pay off an amount of money owed, a plan ofreimbursement, an amount of revenue to be paid to an insurance provider,or some combination thereof, a treatment plan, a monetary value amountgenerated, a risk, etc.), insurance regimens, etc.

The one or more machine learning models 13 may be trained to matchpatterns of characteristics of a patient with characteristics of otherpeople in assigned to a particular cohort. The term “match” may refer toan exact match, a correlative match, a substantial match, etc. The oneor more machine learning models 13 may be trained to receive thecharacteristics of a patient as input, map the characteristics tocharacteristics of people assigned to a cohort, and select a treatmentplan from that cohort. The one or more machine learning models 13 mayalso be trained to control, based on the treatment plan, the machinelearning apparatus 70.

The one or more machine learning models 13 may be trained to matchpatterns of a first set of parameters (e.g., treatment plans forpatients having a medical condition, a set of monetary value amountsassociated with the treatment plans, patient outcome, and/or a set ofconstraints) with a second set of parameters associated with an optimaltreatment plan. The one or more machine learning models 13 may betrained to receive the first set of parameters as input, map thecharacteristics to the second set of parameters associated with theoptimal treatment plan, and select the optimal treatment plan atreatment plan. The one or more machine learning models 13 may also betrained to control, based on the treatment plan, the machine learningapparatus 70.

The one or more machine learning models 13 may be trained to matchpatterns of a first set of parameters (e.g., information pertaining to amedical condition, treatment plans for patients having a medicalcondition, a set of monetary value amounts associated with the treatmentplans, patient outcomes, instructions for the patient to follow in atreatment plan, a set of billing procedures associated with theinstructions, and/or a set of constraints) with a second set ofparameters associated with a billing sequence and/or optimal treatmentplan. The one or more machine learning models 13 may be trained toreceive the first set of parameters as input, map or otherwise associateor algorithmically associate the first set of parameters to the secondset of parameters associated with the billing sequence and/or optimaltreatment plan, and select the billing sequence and/or optimal treatmentplan for the patient. In some embodiments, one or more optimal treatmentplans may be selected to be provided to a computing device of themedical professional and/or the patient. The one or more machinelearning models 13 may also be trained to control, based on thetreatment plan, the machine learning apparatus 70.

Different machine learning models 13 may be trained to recommenddifferent treatment plans tailored for different parameters. Forexample, one machine learning model may be trained to recommendtreatment plans for a maximum monetary value amount generated, whileanother machine learning model may be trained to recommend treatmentplans based on patient outcome, or based on any combination of monetaryvalue amount and patient outcome, or based on those and/or additionalgoals. Also, different machine learning models 13 may be trained torecommend different billing sequences tailored for different parameters.For example, one machine learning model may be trained to recommendbilling sequences for a maximum fee to be paid to a medicalprofessional, while another machine learning model may be trained torecommend billing sequences based on a plan of reimbursement.

Using training data that includes training inputs and correspondingtarget outputs, the one or more machine learning models 13 may refer tomodel artifacts created by the training engine 9. The training engine 9may find patterns in the training data wherein such patterns map thetraining input to the target output, and generate the machine learningmodels 13 that capture these patterns. In some embodiments, theartificial intelligence engine 11, the database 33, and/or the trainingengine 9 may reside on another component (e.g., assistant interface 94,clinician interface 20, etc.) depicted in FIG. 1.

The one or more machine learning models 13 may comprise, e.g., a singlelevel of linear or non-linear operations (e.g., a support vector machine[SVM]) or the machine learning models 13 may be a deep network, i.e., amachine learning model comprising multiple levels of non-linearoperations. Examples of deep networks are neural networks includinggenerative adversarial networks, convolutional neural networks,recurrent neural networks with one or more hidden layers, and fullyconnected neural networks (e.g., each neuron may transmit its outputsignal to the input of the remaining neurons, as well as to itself). Forexample, the machine learning model may include numerous layers and/orhidden layers that perform calculations (e.g., dot products) usingvarious neurons.

The system 10 also includes a patient interface 50 configured tocommunicate information to a patient and to receive feedback from thepatient. Specifically, the patient interface includes an input device 52and an output device 54, which may be collectively called a patient userinterface 52, 54. The input device 52 may include one or more devices,such as a keyboard, a mouse, a touch screen input, a gesture sensor,and/or a microphone and processor configured for voice recognition. Theoutput device 54 may take one or more different forms including, forexample, a computer monitor or display screen on a tablet, smartphone,or a smart watch. The output device 54 may include other hardware and/orsoftware components such as a projector, virtual reality capability,augmented reality capability, etc. The output device 54 may incorporatevarious different visual, audio, or other presentation technologies. Forexample, the output device 54 may include a non-visual display, such asan audio signal, which may include spoken language and/or other soundssuch as tones, chimes, and/or melodies, which may signal differentconditions and/or directions. The output device 54 may comprise one ormore different display screens presenting various data and/or interfacesor controls for use by the patient. The output device 54 may includegraphics, which may be presented by a web-based interface and/or by acomputer program or application (App.).

In some embodiments, the output device 54 may present a user interfacethat may present a recommended treatment plan, billing sequence, or thelike to the patient. The user interface may include one or moregraphical elements that enable the user to select which treatment planto perform. Responsive to receiving a selection of a graphical element(e.g., “Start” button) associated with a treatment plan via the inputdevice 54, the patient interface 50 may communicate a control signal tothe controller 72 of the treatment apparatus, wherein the control signalcauses the treatment apparatus 70 to begin execution of the selectedtreatment plan. As described below, the control signal may control,based on the selected treatment plan, the treatment apparatus 70 bycausing actuation of the actuator 78 (e.g., cause a motor to driverotation of pedals of the treatment apparatus at a certain speed),causing measurements to be obtained via the sensor 76, or the like. Thepatient interface 50 may communicate, via a local communicationinterface 68, the control signal to the treatment apparatus 70.

As shown in FIG. 1, the patient interface 50 includes a secondcommunication interface 56, which may also be called a remotecommunication interface configured to communicate with the server 30and/or the clinician interface 20 via a second network 58. In someembodiments, the second network 58 may include a local area network(LAN), such as an Ethernet network. In some embodiments, the secondnetwork 58 may include the Internet, and communications between thepatient interface 50 and the server 30 and/or the clinician interface 20may be secured via encryption, such as, for example, by using a virtualprivate network (VPN). In some embodiments, the second network 58 mayinclude wired and/or wireless network connections such as Wi-Fi,Bluetooth, ZigBee, Near-Field Communications (NFC), cellular datanetwork, etc. In some embodiments, the second network 58 may be the sameas and/or operationally coupled to the first network 34.

The patient interface 50 includes a second processor 60 and a secondmachine-readable storage memory 62 holding second instructions 64 forexecution by the second processor 60 for performing various actions ofpatient interface 50. The second machine-readable storage memory 62 alsoincludes a local data store 66 configured to hold data, such as datapertaining to a treatment plan and/or patient data, such as datarepresenting a patient's performance within a treatment plan. Thepatient interface 50 also includes a local communication interface 68configured to communicate with various devices for use by the patient inthe vicinity of the patient interface 50. The local communicationinterface 68 may include wired and/or wireless communications. In someembodiments, the local communication interface 68 may include a localwireless network such as Wi-Fi, Bluetooth, ZigBee, Near-FieldCommunications (NFC), cellular data network, etc.

The system 10 also includes a treatment apparatus 70 configured to bemanipulated by the patient and/or to manipulate a body part of thepatient for performing activities according to the treatment plan. Insome embodiments, the treatment apparatus 70 may take the form of anexercise and rehabilitation apparatus configured to perform and/or toaid in the performance of a rehabilitation regimen, which may be anorthopedic rehabilitation regimen, and the treatment includesrehabilitation of a body part of the patient, such as a joint or a boneor a muscle group. The treatment apparatus 70 may be any suitablemedical, rehabilitative, therapeutic, etc. apparatus configured to becontrolled distally via another computing device to treat a patientand/or exercise the patient. The treatment apparatus 70 may be anelectromechanical machine including one or more weights, anelectromechanical bicycle, an electromechanical spin-wheel, asmart-mirror, a treadmill, or the like. The body part may include, forexample, a spine, a hand, a foot, a knee, or a shoulder. The body partmay include a part of a joint, a bone, or a muscle group, such as one ormore vertebrae, a tendon, or a ligament. As shown in FIG. 1, thetreatment apparatus 70 includes a controller 72, which may include oneor more processors, computer memory, and/or other components. Thetreatment apparatus 70 also includes a fourth communication interface 74configured to communicate with the patient interface 50 via the localcommunication interface 68. The treatment apparatus 70 also includes oneor more internal sensors 76 and an actuator 78, such as a motor. Theactuator 78 may be used, for example, for moving the patient's body partand/or for resisting forces by the patient.

The internal sensors 76 may measure one or more operatingcharacteristics of the treatment apparatus 70 such as, for example, aforce a position, a speed, and /or a velocity. In some embodiments, theinternal sensors 76 may include a position sensor configured to measureat least one of a linear motion or an angular motion of a body part ofthe patient. For example, an internal sensor 76 in the form of aposition sensor may measure a distance that the patient is able to movea part of the treatment apparatus 70, where such distance may correspondto a range of motion that the patient's body part is able to achieve. Insome embodiments, the internal sensors 76 may include a force sensorconfigured to measure a force applied by the patient. For example, aninternal sensor 76 in the form of a force sensor may measure a force orweight the patient is able to apply, using a particular body part, tothe treatment apparatus 70.

The system 10 shown in FIG. 1 also includes an ambulation sensor 82,which communicates with the server 30 via the local communicationinterface 68 of the patient interface 50. The ambulation sensor 82 maytrack and store a number of steps taken by the patient. In someembodiments, the ambulation sensor 82 may take the form of a wristband,wristwatch, or smart watch. In some embodiments, the ambulation sensor82 may be integrated within a phone, such as a smartphone.

The system 10 shown in FIG. 1 also includes a goniometer 84, whichcommunicates with the server 30 via the local communication interface 68of the patient interface 50. The goniometer 84 measures an angle of thepatient's body part. For example, the goniometer 84 may measure theangle of flex of a patient's knee or elbow or shoulder.

The system 10 shown in FIG. 1 also includes a pressure sensor 86, whichcommunicates with the server 30 via the local communication interface 68of the patient interface 50. The pressure sensor 86 measures an amountof pressure or weight applied by a body part of the patient. Forexample, pressure sensor 86 may measure an amount of force applied by apatient's foot when pedaling a stationary bike.

The system 10 shown in FIG. 1 also includes a supervisory interface 90which may be similar or identical to the clinician interface 20. In someembodiments, the supervisory interface 90 may have enhancedfunctionality beyond what is provided on the clinician interface 20. Thesupervisory interface 90 may be configured for use by a person havingresponsibility for the treatment plan, such as an orthopedic surgeon.

The system 10 shown in FIG. 1 also includes a reporting interface 92which may be similar or identical to the clinician interface 20. In someembodiments, the reporting interface 92 may have less functionality fromwhat is provided on the clinician interface 20. For example, thereporting interface 92 may not have the ability to modify a treatmentplan. Such a reporting interface 92 may be used, for example, by abiller to determine the use of the system 10 for billing purposes. Inanother example, the reporting interface 92 may not have the ability todisplay patient identifiable information, presenting only pseudonymizeddata and/or anonymized data for certain data fields concerning a datasubject and/or for certain data fields concerning a quasi-identifier ofthe data subject. Such a reporting interface 92 may be used, forexample, by a researcher to determine various effects of a treatmentplan on different patients.

The system 10 includes an assistant interface 94 for an assistant, suchas a doctor, a nurse, a physical therapist, or a technician, to remotelycommunicate with the patient interface 50 and/or the treatment apparatus70. Such remote communications may enable the assistant to provideassistance or guidance to a patient using the system 10. Morespecifically, the assistant interface 94 is configured to communicate atelemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b with the patientinterface 50 via a network connection such as, for example, via thefirst network 34 and/or the second network 58. The telemedicine signal96, 97, 98 a, 98 b, 99 a, 99 b comprises one of an audio signal 96, anaudiovisual signal 97, an interface control signal 98 a for controllinga function of the patient interface 50, an interface monitor signal 98 bfor monitoring a status of the patient interface 50, an apparatuscontrol signal 99 a for changing an operating parameter of the treatmentapparatus 70, and/or an apparatus monitor signal 99 b for monitoring astatus of the treatment apparatus 70. In some embodiments, each of thecontrol signals 98 a, 99 a may be unidirectional, conveying commandsfrom the assistant interface 94 to the patient interface 50. In someembodiments, in response to successfully receiving a control signal 98a, 99 a and/or to communicate successful and/or unsuccessfulimplementation of the requested control action, an acknowledgementmessage may be sent from the patient interface 50 to the assistantinterface 94. In some embodiments, each of the monitor signals 98 b, 99b may be unidirectional, status-information commands from the patientinterface 50 to the assistant interface 94. In some embodiments, anacknowledgement message may be sent from the assistant interface 94 tothe patient interface 50 in response to successfully receiving one ofthe monitor signals 98 b, 99 b.

In some embodiments, the patient interface 50 may be configured as apass-through for the apparatus control signals 99 a and the apparatusmonitor signals 99 b between the treatment apparatus 70 and one or moreother devices, such as the assistant interface 94 and/or the server 30.For example, the patient interface 50 may be configured to transmit anapparatus control signal 99 a in response to an apparatus control signal99 a within the telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b fromthe assistant interface 94.

In some embodiments, the assistant interface 94 may be presented on ashared physical device as the clinician interface 20. For example, theclinician interface 20 may include one or more screens that implementthe assistant interface 94. Alternatively or additionally, the clinicianinterface 20 may include additional hardware components, such as a videocamera, a speaker, and/or a microphone, to implement aspects of theassistant interface 94.

In some embodiments, one or more portions of the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b may be generated from a prerecorded source(e.g., an audio recording, a video recording, or an animation) forpresentation by the output device 54 of the patient interface 50. Forexample, a tutorial video may be streamed from the server 30 andpresented upon the patient interface 50. Content from the prerecordedsource may be requested by the patient via the patient interface 50.Alternatively, via a control on the assistant interface 94, theassistant may cause content from the prerecorded source to be played onthe patient interface 50.

The assistant interface 94 includes an assistant input device 22 and anassistant display 24, which may be collectively called an assistant userinterface 22, 24. The assistant input device 22 may include one or moreof a telephone, a keyboard, a mouse, a trackpad, or a touch screen, forexample. Alternatively or additionally, the assistant input device 22may include one or more microphones. In some embodiments, the one ormore microphones may take the form of a telephone handset, headset, orwide-area microphone or microphones configured for the assistant tospeak to a patient via the patient interface 50. In some embodiments,assistant input device 22 may be configured to provide voice-basedfunctionalities, with hardware and/or software configured to interpretspoken instructions by the assistant by using the one or moremicrophones. The assistant input device 22 may include functionalityprovided by or similar to existing voice-based assistants such as Siriby Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung. Theassistant input device 22 may include other hardware and/or softwarecomponents. The assistant input device 22 may include one or moregeneral purpose devices and/or special-purpose devices.

The assistant display 24 may take one or more different forms including,for example, a computer monitor or display screen on a tablet, asmartphone, or a smart watch. The assistant display 24 may include otherhardware and/or software components such as projectors, virtual realitycapabilities, or augmented reality capabilities, etc. The assistantdisplay 24 may incorporate various different visual, audio, or otherpresentation technologies. For example, the assistant display 24 mayinclude a non-visual display, such as an audio signal, which may includespoken language and/or other sounds such as tones, chimes, melodies,and/or compositions, which may signal different conditions and/ordirections. The assistant display 24 may comprise one or more differentdisplay screens presenting various data and/or interfaces or controlsfor use by the assistant. The assistant display 24 may include graphics,which may be presented by a web-based interface and/or by a computerprogram or application (App.).

In some embodiments, the system 10 may provide computer translation oflanguage from the assistant interface 94 to the patient interface 50and/or vice-versa. The computer translation of language may includecomputer translation of spoken language and/or computer translation oftext. Additionally or alternatively, the system 10 may provide voicerecognition and/or spoken pronunciation of text. For example, the system10 may convert spoken words to printed text and/or the system 10 mayaudibly speak language from printed text. The system 10 may beconfigured to recognize spoken words by any or all of the patient, theclinician, and/or the assistant. In some embodiments, the system 10 maybe configured to recognize and react to spoken requests or commands bythe patient. For example, the system 10 may automatically initiate atelemedicine session in response to a verbal command by the patient(which may be given in any one of several different languages).

In some embodiments, the server 30 may generate aspects of the assistantdisplay 24 for presentation by the assistant interface 94. For example,the server 30 may include a web server configured to generate thedisplay screens for presentation upon the assistant display 24. Forexample, the artificial intelligence engine 11 may generate treatmentplans, billing sequences, and/or excluded treatment plans for patientsand generate the display screens including those treatment plans,billing sequences, and/or excluded treatment plans for presentation onthe assistant display 24 of the assistant interface 94. In someembodiments, the assistant display 24 may be configured to present avirtualized desktop hosted by the server 30. In some embodiments, theserver 30 may be configured to communicate with the assistant interface94 via the first network 34. In some embodiments, the first network 34may include a local area network (LAN), such as an Ethernet network. Insome embodiments, the first network 34 may include the Internet, andcommunications between the server 30 and the assistant interface 94 maybe secured via privacy enhancing technologies, such as, for example, byusing encryption over a virtual private network (VPN). Alternatively oradditionally, the server 30 may be configured to communicate with theassistant interface 94 via one or more networks independent of the firstnetwork 34 and/or other communication means, such as a direct wired orwireless communication channel. In some embodiments, the patientinterface 50 and the treatment apparatus 70 may each operate from apatient location geographically separate from a location of theassistant interface 94. For example, the patient interface 50 and thetreatment apparatus 70 may be used as part of an in-home rehabilitationsystem, which may be aided remotely by using the assistant interface 94at a centralized location, such as a clinic or a call center.

In some embodiments, the assistant interface 94 may be one of severaldifferent terminals (e.g., computing devices) that may be groupedtogether, for example, in one or more call centers or at one or moreclinicians' offices. In some embodiments, a plurality of assistantinterfaces 94 may be distributed geographically. In some embodiments, aperson may work as an assistant remotely from any conventional officeinfrastructure. Such remote work may be performed, for example, wherethe assistant interface 94 takes the form of a computer and/ortelephone. This remote work functionality may allow for work-from-homearrangements that may include part time and/or flexible work hours foran assistant.

FIGS. 2-3 show an embodiment of a treatment apparatus 70. Morespecifically, FIG. 2 shows a treatment apparatus 70 in the form of astationary cycling machine 100, which may be called a stationary bike,for short. The stationary cycling machine 100 includes a set of pedals102 each attached to a pedal arm 104 for rotation about an axle 106. Insome embodiments, and as shown in FIG. 2, the pedals 102 are movable onthe pedal arms 104 in order to adjust a range of motion used by thepatient in pedaling. For example, the pedals being located inwardlytoward the axle 106 corresponds to a smaller range of motion than whenthe pedals are located outwardly away from the axle 106. A pressuresensor 86 is attached to or embedded within one of the pedals 102 formeasuring an amount of force applied by the patient on the pedal 102.The pressure sensor 86 may communicate wirelessly to the treatmentapparatus 70 and/or to the patient interface 50.

FIG. 4 shows a person (a patient) using the treatment apparatus of FIG.2, and showing sensors and various data parameters connected to apatient interface 50. The example patient interface 50 is a tabletcomputer or smartphone, or a phablet, such as an iPad, an iPhone, anAndroid device, or a Surface tablet, which is held manually by thepatient. In some other embodiments, the patient interface 50 may beembedded within or attached to the treatment apparatus 70. FIG. 4 showsthe patient wearing the ambulation sensor 82 on his wrist, with a noteshowing “STEPS TODAY 1355”, indicating that the ambulation sensor 82 hasrecorded and transmitted that step count to the patient interface 50.FIG. 4 also shows the patient wearing the goniometer 84 on his rightknee, with a note showing “KNEE ANGLE 72°”, indicating that thegoniometer 84 is measuring and transmitting that knee angle to thepatient interface 50. FIG. 4 also shows a right side of one of thepedals 102 with a pressure sensor 86 showing “FORCE 12.5 lbs.,”indicating that the right pedal pressure sensor 86 is measuring andtransmitting that force measurement to the patient interface 50. FIG. 4also shows a left side of one of the pedals 102 with a pressure sensor86 showing “FORCE 27 lbs.”, indicating that the left pedal pressuresensor 86 is measuring and transmitting that force measurement to thepatient interface 50. FIG. 4 also shows other patient data, such as anindicator of “SESSION TIME 0:04:13”, indicating that the patient hasbeen using the treatment apparatus 70 for 4 minutes and 13 seconds. Thissession time may be determined by the patient interface 50 based oninformation received from the treatment apparatus 70. FIG. 4 also showsan indicator showing “PAIN LEVEL 3”. Such a pain level may be obtainedfrom the patent in response to a solicitation, such as a question,presented upon the patient interface 50.

FIG. 5 is an example embodiment of an overview display 120 of theassistant interface 94. Specifically, the overview display 120 presentsseveral different controls and interfaces for the assistant to remotelyassist a patient with using the patient interface 50 and/or thetreatment apparatus 70. This remote assistance functionality may also becalled telemedicine or telehealth.

Specifically, the overview display 120 includes a patient profiledisplay 130 presenting biographical information regarding a patientusing the treatment apparatus 70. The patient profile display 130 maytake the form of a portion or region of the overview display 120, asshown in FIG. 5, although the patient profile display 130 may take otherforms, such as a separate screen or a popup window. In some embodiments,the patient profile display 130 may include a limited subset of thepatient's biographical information. More specifically, the datapresented upon the patient profile display 130 may depend upon theassistant's need for that information. For example, a medicalprofessional that is assisting the patient with a medical issue may beprovided with medical history information regarding the patient, whereasa technician troubleshooting an issue with the treatment apparatus 70may be provided with a much more limited set of information regardingthe patient. The technician, for example, may be given only thepatient's name. The patient profile display 130 may includepseudonymized data and/or anonymized data or use any privacy enhancingtechnology to prevent confidential patient data from being communicatedin a way that could violate patient confidentiality requirements. Suchprivacy enhancing technologies may enable compliance with laws,regulations, or other rules of governance such as, but not limited to,the Health Insurance Portability and Accountability Act (HIPAA), or theGeneral Data Protection Regulation (GDPR), wherein the patient may bedeemed a “data subject”.

In some embodiments, the patient profile display 130 may presentinformation regarding the treatment plan for the patient to follow inusing the treatment apparatus 70. Such treatment plan information may belimited to an assistant who is a medical professional, such as a doctoror physical therapist. For example, a medical professional assisting thepatient with an issue regarding the treatment regimen may be providedwith treatment plan information, whereas a technician troubleshooting anissue with the treatment apparatus 70 may not be provided with anyinformation regarding the patient's treatment plan.

In some embodiments, one or more recommended treatment plans and/orexcluded treatment plans may be presented in the patient profile display130 to the assistant. The one or more recommended treatment plans and/orexcluded treatment plans may be generated by the artificial intelligenceengine 11 of the server 30 and received from the server 30 in real-timeduring, inter alia, a telemedicine or telehealth session. An example ofpresenting the one or more recommended treatment plans and/or ruled-outtreatment plans is described below with reference to FIG. 7.

In some embodiments, one or more treatment plans and/or billingsequences associated with the treatment plans may be presented in thepatient profile display 130 to the assistant. The one or more treatmentplans and/or billing sequences associated with the treatment plans maybe generated by the artificial intelligence engine 11 of the server 30and received from the server 30 in real-time during, inter alia, atelehealth session. An example of presenting the one or more treatmentplans and/or billing sequences associated with the treatment plans isdescribed below with reference to FIG. 9.

In some embodiments, one or more treatment plans and associated monetaryvalue amounts generated, patient outcomes, and risks associated with thetreatment plans may be presented in the patient profile display 130 tothe assistant. The one or more treatment plans and associated monetaryvalue amounts generated, patient outcomes, and risks associated with thetreatment plans may be generated by the artificial intelligence engine11 of the server 30 and received from the server 30 in real-time during,inter alia, a telehealth session. An example of presenting the one ormore treatment plans and associated monetary value amounts generated,patient outcomes, and risks associated with the treatment plans isdescribed below with reference to FIG. 12.

The example overview display 120 shown in FIG. 5 also includes a patientstatus display 134 presenting status information regarding a patientusing the treatment apparatus. The patient status display 134 may takethe form of a portion or region of the overview display 120, as shown inFIG. 5, although the patient status display 134 may take other forms,such as a separate screen or a popup window. The patient status display134 includes sensor data 136 from one or more of the external sensors82, 84, 86, and/or from one or more internal sensors 76 of the treatmentapparatus 70. In some embodiments, the patient status display 134 maypresent other data 138 regarding the patient, such as last reported painlevel, or progress within a treatment plan.

User access controls may be used to limit access, including what data isavailable to be viewed and/or modified, on any or all of the userinterfaces 20, 50, 90, 92, 94 of the system 10. In some embodiments,user access controls may be employed to control what information isavailable to any given person using the system 10. For example, datapresented on the assistant interface 94 may be controlled by user accesscontrols, with permissions set depending on the assistant/user's needfor and/or qualifications to view that information.

The example overview display 120 shown in FIG. 5 also includes a helpdata display 140 presenting information for the assistant to use inassisting the patient. The help data display 140 may take the form of aportion or region of the overview display 120, as shown in FIG. 5. Thehelp data display 140 may take other forms, such as a separate screen ora popup window. The help data display 140 may include, for example,presenting answers to frequently asked questions regarding use of thepatient interface 50 and/or the treatment apparatus 70. The help datadisplay 140 may also include research data or best practices. In someembodiments, the help data display 140 may present scripts for answersor explanations in response to patient questions. In some embodiments,the help data display 140 may present flow charts or walk-throughs forthe assistant to use in determining a root cause and/or solution to apatient's problem. In some embodiments, the assistant interface 94 maypresent two or more help data displays 140, which may be the same ordifferent, for simultaneous presentation of help data for use by theassistant. for example, a first help data display may be used to presenta troubleshooting flowchart to determine the source of a patient'sproblem, and a second help data display may present script informationfor the assistant to read to the patient, such information to preferablyinclude directions for the patient to perform some action, which mayhelp to narrow down or solve the problem. In some embodiments, basedupon inputs to the troubleshooting flowchart in the first help datadisplay, the second help data display may automatically populate withscript information.

The example overview display 120 shown in FIG. 5 also includes a patientinterface control 150 presenting information regarding the patientinterface 50, and/or to modify one or more settings of the patientinterface 50. The patient interface control 150 may take the form of aportion or region of the overview display 120, as shown in FIG. 5. Thepatient interface control 150 may take other forms, such as a separatescreen or a popup window. The patient interface control 150 may presentinformation communicated to the assistant interface 94 via one or moreof the interface monitor signals 98 b. As shown in FIG. 5, the patientinterface control 150 includes a display feed 152 of the displaypresented by the patient interface 50. In some embodiments, the displayfeed 152 may include a live copy of the display screen currently beingpresented to the patient by the patient interface 50. In other words,the display feed 152 may present an image of what is presented on adisplay screen of the patient interface 50. In some embodiments, thedisplay feed 152 may include abbreviated information regarding thedisplay screen currently being presented by the patient interface 50,such as a screen name or a screen number. The patient interface control150 may include a patient interface setting control 154 for theassistant to adjust or to control one or more settings or aspects of thepatient interface 50. In some embodiments, the patient interface settingcontrol 154 may cause the assistant interface 94 to generate and/or totransmit an interface control signal 98 for controlling a function or asetting of the patient interface 50.

In some embodiments, the patient interface setting control 154 mayinclude collaborative browsing or co-browsing capability for theassistant to remotely view and/or control the patient interface 50. Forexample, the patient interface setting control 154 may enable theassistant to remotely enter text to one or more text entry fields on thepatient interface 50 and/or to remotely control a cursor on the patientinterface 50 using a mouse or touchscreen of the assistant interface 94.

In some embodiments, using the patient interface 50, the patientinterface setting control 154 may allow the assistant to change asetting that cannot be changed by the patient. For example, the patientinterface 50 may be precluded from accessing a language setting toprevent a patient from inadvertently switching, on the patient interface50, the language used for the displays, whereas the patient interfacesetting control 154 may enable the assistant to change the languagesetting of the patient interface 50. In another example, the patientinterface 50 may not be able to change a font size setting to a smallersize in order to prevent a patient from inadvertently switching the fontsize used for the displays on the patient interface 50 such that thedisplay would become illegible to the patient, whereas the patientinterface setting control 154 may provide for the assistant to changethe font size setting of the patient interface 50.

The example overview display 120 shown in FIG. 5 also includes aninterface communications display 156 showing the status ofcommunications between the patient interface 50 and one or more otherdevices 70, 82, 84, such as the treatment apparatus 70, the ambulationsensor 82, and/or the goniometer 84. The interface communicationsdisplay 156 may take the form of a portion or region of the overviewdisplay 120, as shown in FIG. 5. The interface communications display156 may take other forms, such as a separate screen or a popup window.The interface communications display 156 may include controls for theassistant to remotely modify communications with one or more of theother devices 70, 82, 84. For example, the assistant may remotelycommand the patient interface 50 to reset communications with one of theother devices 70, 82, 84, or to establish communications with a new oneof the other devices 70, 82, 84. This functionality may be used, forexample, where the patient has a problem with one of the other devices70, 82, 84, or where the patient receives a new or a replacement one ofthe other devices 70, 82, 84.

The example overview display 120 shown in FIG. 5 also includes anapparatus control 160 for the assistant to view and/or to controlinformation regarding the treatment apparatus 70. The apparatus control160 may take the form of a portion or region of the overview display120, as shown in FIG. 5. The apparatus control 160 may take other forms,such as a separate screen or a popup window. The apparatus control 160may include an apparatus status display 162 with information regardingthe current status of the apparatus. The apparatus status display 162may present information communicated to the assistant interface 94 viaone or more of the apparatus monitor signals 99 b. The apparatus statusdisplay 162 may indicate whether the treatment apparatus 70 is currentlycommunicating with the patient interface 50. The apparatus statusdisplay 162 may present other current and/or historical informationregarding the status of the treatment apparatus 70.

The apparatus control 160 may include an apparatus setting control 164for the assistant to adjust or control one or more aspects of thetreatment apparatus 70. The apparatus setting control 164 may cause theassistant interface 94 to generate and/or to transmit an apparatuscontrol signal 99 for changing an operating parameter of the treatmentapparatus 70, (e.g., a pedal radius setting, a resistance setting, atarget RPM, etc.). The apparatus setting control 164 may include a modebutton 166 and a position control 168, which may be used in conjunctionfor the assistant to place an actuator 78 of the treatment apparatus 70in a manual mode, after which a setting, such as a position or a speedof the actuator 78, can be changed using the position control 168. Themode button 166 may provide for a setting, such as a position, to betoggled between automatic and manual modes. In some embodiments, one ormore settings may be adjustable at any time, and without having anassociated auto/manual mode. In some embodiments, the assistant maychange an operating parameter of the treatment apparatus 70, such as apedal radius setting, while the patient is actively using the treatmentapparatus 70. Such “on the fly” adjustment may or may not be availableto the patient using the patient interface 50. In some embodiments, theapparatus setting control 164 may allow the assistant to change asetting that cannot be changed by the patient using the patientinterface 50. For example, the patient interface 50 may be precludedfrom changing a preconfigured setting, such as a height or a tiltsetting of the treatment apparatus 70, whereas the apparatus settingcontrol 164 may provide for the assistant to change the height or tiltsetting of the treatment apparatus 70.

The example overview display 120 shown in FIG. 5 also includes a patientcommunications control 170 for controlling an audio or an audiovisualcommunications session with the patient interface 50. The communicationssession with the patient interface 50 may comprise a live feed from theassistant interface 94 for presentation by the output device of thepatient interface 50. The live feed may take the form of an audio feedand/or a video feed. In some embodiments, the patient interface 50 maybe configured to provide two-way audio or audiovisual communicationswith a person using the assistant interface 94. Specifically, thecommunications session with the patient interface 50 may includebidirectional (two-way) video or audiovisual feeds, with each of thepatient interface 50 and the assistant interface 94 presenting video ofthe other one. In some embodiments, the patient interface 50 may presentvideo from the assistant interface 94, while the assistant interface 94presents only audio or the assistant interface 94 presents no live audioor visual signal from the patient interface 50. In some embodiments, theassistant interface 94 may present video from the patient interface 50,while the patient interface 50 presents only audio or the patientinterface 50 presents no live audio or visual signal from the assistantinterface 94.

In some embodiments, the audio or an audiovisual communications sessionwith the patient interface 50 may take place, at least in part, whilethe patient is performing the rehabilitation regimen upon the body part.The patient communications control 170 may take the form of a portion orregion of the overview display 120, as shown in FIG. 5. The patientcommunications control 170 may take other forms, such as a separatescreen or a popup window. The audio and/or audiovisual communicationsmay be processed and/or directed by the assistant interface 94 and/or byanother device or devices, such as a telephone system, or avideoconferencing system used by the assistant while the assistant usesthe assistant interface 94. Alternatively or additionally, the audioand/or audiovisual communications may include communications with athird party. For example, the system 10 may enable the assistant toinitiate a 3-way conversation regarding use of a particular piece ofhardware or software, with the patient and a subject matter expert, suchas a medical professional or a specialist. The example patientcommunications control 170 shown in FIG. 5 includes call controls 172for the assistant to use in managing various aspects of the audio oraudiovisual communications with the patient. The call controls 172include a disconnect button 174 for the assistant to end the audio oraudiovisual communications session. The call controls 172 also include amute button 176 to temporarily silence an audio or audiovisual signalfrom the assistant interface 94. In some embodiments, the call controls172 may include other features, such as a hold button (not shown). Thecall controls 172 also include one or more record/playback controls 178,such as record, play, and pause buttons to control, with the patientinterface 50, recording and/or playback of audio and/or video from theteleconference session. The call controls 172 also include a video feeddisplay 180 for presenting still and/or video images from the patientinterface 50, and a self-video display 182 showing the current image ofthe assistant using the assistant interface. The self-video display 182may be presented as a picture-in-picture format, within a section of thevideo feed display 180, as shown in FIG. 5. Alternatively oradditionally, the self-video display 182 may be presented separatelyand/or independently from the video feed display 180.

The example overview display 120 shown in FIG. 5 also includes a thirdparty communications control 190 for use in conducting audio and/oraudiovisual communications with a third party. The third partycommunications control 190 may take the form of a portion or region ofthe overview display 120, as shown in FIG. 5. The third partycommunications control 190 may take other forms, such as a display on aseparate screen or a popup window. The third party communicationscontrol 190 may include one or more controls, such as a contact listand/or buttons or controls to contact a third party regarding use of aparticular piece of hardware or software, e.g., a subject matter expert,such as a medical professional or a specialist. The third partycommunications control 190 may include conference calling capability forthe third party to simultaneously communicate with both the assistantvia the assistant interface 94, and with the patient via the patientinterface 50. For example, the system 10 may provide for the assistantto initiate a 3-way conversation with the patient and the third party.

FIG. 6 shows an example block diagram of training a machine learningmodel 13 to output, based on data 600 pertaining to the patient, atreatment plan 602 for the patient according to the present disclosure.Data pertaining to other patients may be received by the server 30. Theother patients may have used various treatment apparatuses to performtreatment plans. The data may include characteristics of the otherpatients, the details of the treatment plans performed by the otherpatients, and/or the results of performing the treatment plans (e.g., apercent of recovery of a portion of the patients' bodies, an amount ofrecovery of a portion of the patients' bodies, an amount of increase ordecrease in muscle strength of a portion of patients' bodies, an amountof increase or decrease in range of motion of a portion of patients'bodies, etc.).

As depicted, the data has been assigned to different cohorts. Cohort Aincludes data for patients having similar first characteristics, firsttreatment plans, and first results. Cohort B includes data for patientshaving similar second characteristics, second treatment plans, andsecond results. For example, cohort A may include first characteristicsof patients in their twenties without any medical conditions whounderwent surgery for a broken limb; their treatment plans may include acertain treatment protocol (e.g., use the treatment apparatus 70 for 30minutes 5 times a week for 3 weeks, wherein values for the properties,configurations, and/or settings of the treatment apparatus 70 are set toX (where X is a numerical value) for the first two weeks and to Y (whereY is a numerical value) for the last week).

Cohort A and cohort B may be included in a training dataset used totrain the machine learning model 13. The machine learning model 13 maybe trained to match a pattern between characteristics for each cohortand output the treatment plan that provides the result. Accordingly,when the data 600 for a new patient is input into the trained machinelearning model 13, the trained machine learning model 13 may match thecharacteristics included in the data 600 with characteristics in eithercohort A or cohort B and output the appropriate treatment plan 602. Insome embodiments, the machine learning model 13 may be trained to outputone or more excluded treatment plans that should not be performed by thenew patient.

FIG. 7 shows an embodiment of an overview display 120 of the assistantinterface 94 presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according tothe present disclosure. As depicted, the overview display 120 justincludes sections for the patient profile 130 and the video feed display180, including the self-video display 182. Any suitable configuration ofcontrols and interfaces of the overview display 120 described withreference to FIG. 5 may be presented in addition to or instead of thepatient profile 130, the video feed display 180, and the self-videodisplay 182.

The assistant (e.g., medical professional) using the assistant interface94 (e.g., computing device) during the telemedicine session may bepresented in the self-video 182 in a portion of the overview display 120(e.g., user interface presented on a display screen 24 of the assistantinterface 94) that also presents a video from the patient in the videofeed display 180. Further, the video feed display 180 may also include agraphical user interface (GUI) object 700 (e.g., a button) that enablesthe medical professional to share, in real-time or near real-time duringthe telemedicine session, the recommended treatment plans and/or theexcluded treatment plans with the patient on the patient interface 50.The medical professional may select the GUI object 700 to share therecommended treatment plans and/or the excluded treatment plans. Asdepicted, another portion of the overview display 120 includes thepatient profile display 130.

The patient profile display 130 is presenting two example recommendedtreatment plans 600 and one example excluded treatment plan 602. Asdescribed herein, the treatment plans may be recommended in view ofcharacteristics of the patient being treated. To generate therecommended treatment plans 600 the patient should follow to achieve adesired result, a pattern between the characteristics of the patientbeing treated and a cohort of other people who have used the treatmentapparatus 70 to perform a treatment plan may be matched by one or moremachine learning models 13 of the artificial intelligence engine 11.Each of the recommended treatment plans may be generated based ondifferent desired results.

For example, as depicted, the patient profile display 130 presents “Thecharacteristics of the patient match characteristics of users in CohortA. The following treatment plans are recommended for the patient basedon his characteristics and desired results.” Then, the patient profiledisplay 130 presents recommended treatment plans from cohort A, and eachtreatment plan provides different results.

As depicted, treatment plan “A” indicates “Patient X should usetreatment apparatus for 30 minutes a day for 4 days to achieve anincreased range of motion of Y %; Patient X has Type 2 Diabetes; andPatient X should be prescribed medication Z for pain management duringthe treatment plan (medication Z is approved for people having Type 2Diabetes).” Accordingly, the treatment plan generated achievesincreasing the range of motion of Y %. As may be appreciated, thetreatment plan also includes a recommended medication (e.g., medicationZ) to prescribe to the patient to manage pain in view of a known medicaldisease (e.g., Type 2 Diabetes) of the patient. That is, the recommendedpatient medication not only does not conflict with the medical conditionof the patient but thereby improves the probability of a superiorpatient outcome. This specific example and all such examples elsewhereherein are not intended to limit in any way the generated treatment planfrom recommending multiple medications, or from handling theacknowledgement, view, diagnosis and/or treatment of comorbid conditionsor diseases.

Recommended treatment plan “B” may specify, based on a different desiredresult of the treatment plan, a different treatment plan including adifferent treatment protocol for a treatment apparatus, a differentmedication regimen, etc.

As depicted, the patient profile display 130 may also present theexcluded treatment plans 602. These types of treatment plans are shownto the assistant using the assistant interface 94 to alert the assistantnot to recommend certain portions of a treatment plan to the patient.For example, the excluded treatment plan could specify the following:“Patient X should not use treatment apparatus for longer than 30 minutesa day due to a heart condition; Patient X has Type 2 Diabetes; andPatient X should not be prescribed medication M for pain managementduring the treatment plan (in this scenario, medication M can causecomplications for people having Type 2 Diabetes). Specifically, theexcluded treatment plan points out a limitation of a treatment protocolwhere, due to a heart condition, Patient X should not exercise for morethan 30 minutes a day. The ruled-out treatment plan also points out thatPatient X should not be prescribed medication M because it conflictswith the medical condition Type 2 Diabetes.

The assistant may select the treatment plan for the patient on theoverview display 120. For example, the assistant may use an inputperipheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) toselect from the treatment plans 600 for the patient. In someembodiments, during the telemedicine session, the assistant may discussthe pros and cons of the recommended treatment plans 600 with thepatient.

In any event, the assistant may select the treatment plan for thepatient to follow to achieve the desired result. The selected treatmentplan may be transmitted to the patient interface 50 for presentation.The patient may view the selected treatment plan on the patientinterface 50. In some embodiments, the assistant and the patient maydiscuss during the telemedicine session the details (e.g., treatmentprotocol using treatment apparatus 70, diet regimen, medication regimen,etc.) in real-time or in near real-time. In some embodiments, the server30 may control, based on the selected treatment plan and during thetelemedicine session, the treatment apparatus 70 as the user uses thetreatment apparatus 70.

FIG. 8 shows an embodiment of the overview display 120 of the assistantinterface 94 presenting, in real-time during a telemedicine session,recommended treatment plans that have changed as a result of patientdata changing according to the present disclosure. As may beappreciated, the treatment apparatus 70 and/or any computing device(e.g., patient interface 50) may transmit data while the patient usesthe treatment apparatus 70 to perform a treatment plan. The data mayinclude updated characteristics of the patient. For example, the updatedcharacteristics may include new performance information and/ormeasurement information. The performance information may include a speedof a portion of the treatment apparatus 70, a range of motion achievedby the patient, a force exerted on a portion of the treatment apparatus70, a heartrate of the patient, a blood pressure of the patient, arespiratory rate of the patient, and so forth.

In one embodiment, the data received at the server 30 may be input intothe trained machine learning model 13, which may determine that thecharacteristics indicate the patient is on track for the currenttreatment plan. Determining the patient is on track for the currenttreatment plan may cause the trained machine learning model 13 to adjusta parameter of the treatment apparatus 70. The adjustment may be basedon a next step of the treatment plan to further improve the performanceof the patient.

In one embodiment, the data received at the server 30 may be input intothe trained machine learning model 13, which may determine that thecharacteristics indicate the patient is not on track (e.g., behindschedule, not able to maintain a speed, not able to achieve a certainrange of motion, is in too much pain, etc.) for the current treatmentplan or is ahead of schedule (e.g., exceeding a certain speed,exercising longer than specified with no pain, exerting more than aspecified force, etc.) for the current treatment plan. The trainedmachine learning model 13 may determine that the characteristics of thepatient no longer match the characteristics of the patients in thecohort to which the patient is assigned. Accordingly, the trainedmachine learning model 13 may reassign the patient to another cohortthat includes qualifying characteristics the patient's characteristics.As such, the trained machine learning model 13 may select a newtreatment plan from the new cohort and control, based on the newtreatment plan, the treatment apparatus 70.

In some embodiments, prior to controlling the treatment apparatus 70,the server 30 may provide the new treatment plan 800 to the assistantinterface 94 for presentation in the patient profile 130. As depicted,the patient profile 130 indicates “The characteristics of the patienthave changed and now match characteristics of users in Cohort B. Thefollowing treatment plan is recommended for the patient based on hischaracteristics and desired results.” Then, the patient profile 130presents the new treatment plan 800 (“Patient X should use treatmentapparatus for 10 minutes a day for 3 days to achieve an increased rangeof motion of L %” The assistant (medical professional) may select thenew treatment plan 800, and the server 30 may receive the selection. Theserver 30 may control the treatment apparatus 70 based on the newtreatment plan 800. In some embodiments, the new treatment plan 800 maybe transmitted to the patient interface 50 such that the patient mayview the details of the new treatment plan 800.

FIG. 9 shows an embodiment of the overview display 120 of the assistantinterface 94 presenting, in real-time during a telemedicine session,treatment plans and billing sequences tailored for certain parametersaccording to the present disclosure. As depicted, the overview display120 just includes sections for the patient profile 130 and the videofeed display 180, including the self-video display 182. Any suitableconfiguration of controls and interfaces of the overview display 120described with reference to FIG. 5 may be presented in addition to orinstead of the patient profile 130, the video feed display 180, and theself-video display 182. In some embodiments, the same treatment plansand billing sequences may be presented in a display screen 54 of thepatient interface 50. In some embodiments, the treatment plans andbilling sequences may be presented simultaneously, in real-time or nearreal-time, during a telemedicine or telehealth session, on both thedisplay screen 54 of the patient interface 50 and the display screen 24of the assistant interface 94.

The assistant (e.g., medical professional) using the assistant interface94 (e.g., computing device) during the telemedicine session may bepresented in the self-video 182 in a portion of the overview display 120(e.g., user interface presented on a display screen 24 of the assistantinterface 94) that also presents a video from the patient in the videofeed display 180. Further, the video feed display 180 may also include agraphical user interface (GUI) object 700 (e.g., a button) that enablesthe medical professional to share, in real-time or near real-time duringthe telemedicine session, the treatment plans and/or the billingsequences with the patient on the patient interface 50. The medicalprofessional may select the GUI object 700 to share the treatment plansand/or the billing sequences. As depicted, another portion of theoverview display 120 includes the patient profile display 130.

The patient profile display 130 is presenting two example treatmentplans and two example billing sequences. Treatment plans 900 and 902 maybe generated based on information (e.g., medical diagnosis code)pertaining to a condition of the patient. Treatment plan 900 correspondsto billing sequence 904, and treatment plan 902 corresponds to billingsequence 906. The generated billing sequences 904 and 906 and thetreatment plans 900 and 902 comply with a set of billing proceduresincluding rules pertaining to billing codes, order, timing, andconstraints (e.g., laws, regulations, etc.). As described herein, eachof the respective the billing sequences 904 and 906 may be generatedbased on a set of billing procedures associated with at least a portionof instructions included in each of the respective treatment plans 900and 902. Further, each of the billing sequences 904 and 906 and/ortreatment plans 900 and 902 may be tailored according to a certainparameter (e.g., a fee to be paid to a medical professional, a paymentplan for the patient to pay off an amount of money owed, a plan ofreimbursement, a monetary value amount to be paid to an insuranceprovider, or some combination thereof). In some embodiments, themonetary value amount “to be paid” may be inclusive to any means ofsettling an account with an insurance provider (e.g., payment ofmonetary, issuance of credit).

Each of the respective treatment plans 900 and 902 may include one ormore procedures to be performed on the patient based on the informationpertaining to the medical condition of the patient. Further, each of therespective billing sequences 904 and 906 may include an order for howthe procedures are to be billed based on the billing procedures and oneor more parameters.

For example, as depicted, the patient profile display 130 presents“Patient has Condition Z”, where condition Z may be associated withinformation of the patient including a particular medical diagnosis codereceived from an EMR system. Based on the information, the treatmentplans 900 and 906 each include procedures relevant to be performed forthe Condition Z. The patient profile 130 presents “Treatment Plan 1: 1.Procedure A; 2. Procedure B”. Each of the procedures may specify one ormore instructions for performing the procedures, and each of the one ormore instructions may be associated with a particular billing code orcodes. Then, the patient profile display 130 presents the billingsequence 904 generated, based on the billing procedures and one or moreparameters, for at least a portion of the one or more instructionsincluded in the treatment plan 900. The patient profile display 130presents “Billing Sequence 1 Tailored for [Parameter X]: 1. Bill forcode 123 associated with Procedure A; 2. Bill for code 234 associatedwith Procedure B”. It should be noted that [Parameter X] may be anysuitable parameter, such as a fee to be paid to a medical professional,a payment plan for the patient to pay off an amount of money owed, aplan of reimbursement, a monetary value amount to be paid to aninsurance provider, or some combination thereof.

Further, the patient profile 130 also presents the treatment plan 902and presents “Treatment Plan 2: 1. Procedure C; 2. Procedure A”. Each ofthe procedures may specify one or more instructions for performing theprocedures, and each of the one or more instructions may be associatedwith a particular billing code. Then, the patient profile display 130presents the billing sequence 906 generated, based on the billingprocedures and one or more parameters, for at least a portion of the oneor more instructions included in the treatment plan 902. The patientprofile display 130 presents “Billing Sequence 2 Tailored for [ParameterY]: 1. Bill for code 345 associated with Procedure C; 2. Bill for code123 associated with Procedure A”. It should be noted that [Parameter Y]may be any suitable parameter, such as a fee to be paid to a medicalprofessional, a payment plan for the patient to pay off an amount ofmoney owed, a plan of reimbursement, a monetary value amount to be paidto an insurance provider, or some combination thereof. It should also benoted that in the depicted example [Parameter X] and [Parameter Y] aredifferent parameters.

As should be appreciated, the billing sequence 904 and 906 includes adifferent order for billing the procedures included in the respectivetreatment plans 900 902, and each of the billing sequences 904 and 906complies with the billing procedures. The billing sequence 904 may havebeen tailored for [Parameter X] (e.g., a fee to be paid to a medicalprofessional) and the billing sequence 906 may have been tailored for[Parameter Y] (e.g., a plan of reimbursement).

The order of performing the procedures for the treatment plan 902specifies performing Procedure C first and then Procedure A. However,the billing sequence 906 specifies billing for the code 123 associatedwith Procedure A first and then billing for the code 345 associated withProcedure C. Such a billing sequence 906 may have been dictated by thebilling procedures. For example, although Procedure A is performedsecond, a law, regulation, or the like may dictate that Procedure A bebilled before any other procedure.

Further, as depicted, a graphical element (e.g., button for “SELECT”)may be presented in the patient profile display 130. Although just onegraphical element is presented, any suitable number of graphicalelements for selecting a treatment plan and/or billing sequence may bepresented in the patient profile display 130. As depicted, a user (e.g.,medical professional or patient) uses an input peripheral (e.g., mouse,keyboard, microphone, touchscreen) to select (as represented by circle950) the graphical element associated with the treatment plan 900 andbilling sequence 904. The medical professional may prefer to receive acertain fee and the billing sequence 904 is optimized based on[Parameter X] (e.g., a fee to be paid to the medical professional, aspreviously discussed). Accordingly, the assistant interface 94 maytransmit a control signal to the treatment apparatus 70 to control,based on the treatment plan 900, operation of the treatment apparatus70. In some embodiments, the patient may select the treatment plan fromthe display screen 54 and the patient interface 50 may transmit acontrol signal to the treatment apparatus 70 to control, based on theselected treatment plan, operation of the treatment apparatus 70.

FIG. 10 shows an example embodiment of a method 1000 for generating,based on a set of billing procedures, a billing sequence tailored for aparticular parameter, where the billing sequence pertains to a treatmentplan according to the present disclosure. The method 1000 is performedby processing logic that may include hardware (circuitry, dedicatedlogic, etc.), software (such as is run on a general-purpose computersystem or a dedicated machine), or a combination of both. The method1000 and/or each of its individual functions, routines, other methods,scripts, subroutines, or operations may be performed by one or moreprocessors of a computing device (e.g., any component of FIG. 1, such asserver 30 executing the artificial intelligence engine 11). In certainimplementations, the method 1000 may be performed by a single processingthread. Alternatively, the method 1000 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, other methods, scripts, subroutines, or operationsof the methods.

For simplicity of explanation, the method 1000 is depicted and describedas a series of operations. However, operations in accordance with thisdisclosure can occur in various orders and/or concurrently, and/or withother operations not presented and described herein. For example, theoperations depicted in the method 1000 may occur in combination with anyother operation of any other method disclosed herein. Furthermore, notall illustrated operations may be required to implement the method 1000in accordance with the disclosed subject matter. In addition, thoseskilled in the art will understand and appreciate that the method 1000could alternatively be represented as a series of interrelated statesvia a state diagram, a directed graph, a deterministic finite stateautomaton, a non-deterministic finite state automaton, a Markov diagram,or events.

At 1002, the processing device may receive information pertaining to apatient. The information may include a medical diagnosis code (DRG,ICD-9, ICD-10, etc.) associated with the patient. The information mayalso include characteristics of the patient, such as personalinformation, performance information, and measurement information. Thepersonal information may include, e.g., demographic, psychographic orother information, such as an age, a weight, a gender, a height, a bodymass index, a medical condition, a familial medication history, aninjury, a medical procedure, a medication prescribed, or somecombination thereof. The performance information may include, e.g., anelapsed time of using a treatment apparatus, an amount of force exertedon a portion of the treatment apparatus, the body part used to exert theamount of force, the tendons, ligaments, muscles and other body partsassociated with or connected to the body part, a range of motionachieved on the treatment apparatus, a movement speed of a portion ofthe treatment apparatus, an indication of a plurality of pain levelsusing the treatment apparatus, or some combination thereof. Themeasurement information may include, e.g., a vital sign, a respirationrate, a heartrate, a temperature, a blood pressure, or some combinationthereof. It may be desirable to process the characteristics of amultitude of patients, the treatment plans performed for those patients,and the results of the treatment plans for those patients.

At 1004, the processing device may generate, based on the information, atreatment plan for the patient. The treatment plan may include a set ofinstructions for the patient to follow (e.g., for rehabilitation,prehabilitation, post-habilitation, etc.). In some embodiments, thetreatment plan may be generated by comparing and matching theinformation of the patient with information of other patients. In someembodiments, the treatment plan may pertain to habilitation,prehabilitation, rehabilitation, post-habilitation, exercise, strengthtraining, endurance training, weight loss, weight gain, flexibility,pliability, or some combination thereof. In some embodiments, the set ofinstructions may include a set of exercises for the patient to perform,an order for the set of exercises, a frequency for performing the set ofexercises, a diet program, a sleep regimen, a set of procedures toperform on the patient, an order for the set of procedures, a medicationregimen, a set of sessions for the patient, or some combination thereof.

At 1006, the processing device may receive a set of billing proceduresassociated with the set of instructions. The set of billing proceduresmay include rules pertaining to billing codes, timing, order, insuranceregimens, constraints, or some combination thereof. In some embodiments,the constraints may include constraints set forth in regulations, laws,or some combination thereof. The rules pertaining to the billing codesmay specify exact billing codes for procedures. The billing codes may bestandardized and mandated by certain regulatory agencies and/or systems.A certain billing code may be unique to a certain procedure.

The rules pertaining to the timing information may specify when certainprocedures and/or associated billing codes may be billed. The timinginformation may also specify a length of time from when a procedure isperformed until the procedure can be billed, a periodicity that certainprocedures may be billed, a frequency that certain procedures may bebilled, and so forth.

The rules pertaining to the order information may specify an order inwhich certain procedures and/or billing codes may be billed to thepatient. For example, the rules may specify that a certain procedurecannot be billed until another procedure is billed.

The rules pertaining to the insurance regimens may specify what amountand/or percentage the insurance provider pays based on the insurancebenefits of the patient, when the insurance provider distributespayments, and the like.

The rules pertaining to the constraints may include laws and regulationsof medical billing. For example, the Health Insurance Portability andAccountability Act (HIPAA) includes numerous medical billing laws andregulations. In the European Union, the General Protection DataRegulation (GDPR) would impose certain constraints. One of the laws andregulations is patient confidentiality, which makes it necessary foreach and every medical practice to create safeguards against the leakingof confidential patient information. Another of the laws and regulationsis the use of ICD-10 codes, which allow for more specificity inreporting of patient diagnoses. Other laws and regulations, in certainjurisdictions, may include requirements to pseudonymize, pseudonymise,anonymize or anonymise (the terms can have different meanings indifferent countries and jurisidictions) data subject (i.e., patient)personally identifying information (PII) or personal health identifyinginformation (PHI).

Another law and regulation pertains to balance billing. When ahealthcare provider signs a contract with an insurance company, thehealthcare provider agrees to take a certain percentage or paymentamount for specific services. The amount the healthcare provider billsover the agreed upon amount with the insurance provider must be writtenoff by the healthcare provider's office. That is, the healthcareprovider cannot bill the patient for any amount over the negotiatedrate. If, nevertheless, a healthcare provider does this, it is referredto as balance billing, which is illegal per the contract with theinsurance company.

Further, medical billing fraud is also specified as being illegal byHIPAA. Medical billing fraud may refer to a healthcare provider's officeknowingly billing for services that were not performed, or that areinaccurately represented or described.

At 1008, the processing device may generate, based on the set of billingprocedures, a billing sequence for at least a portion of the set ofinstructions included in the treatment plan. Just a portion of the totalnumber of instructions may be accounted for in the billing sequencebecause some of the instructions may not yet have been completed or maystill be completed in the future. However, if all the instructionsincluded in the treatment plan are completed, then the billing sequencemay be generated for all of the instructions. The billing sequence maybe tailored according to a certain parameter. The parameter may be a feeto be paid to a medical professional, a payment plan for the patient topay off an amount of money owed, a plan of reimbursement, a monetaryvalue amount to be paid to an insurance provider, or some combinationthereof.

At 1010, the processing device may transmit the treatment plan and thebilling sequence to a computing device. The computing device may be anyof the interfaces described with reference to FIG. 1. For example, thetreatment plan and the billing sequence may be transmitted to anassistant interface 94 and/or a patient interface 50.

In some embodiments, the processing device may cause presentation, inreal-time or near real-time during a telemedicine session with acomputing device of the patient, of the treatment plan and the billingsequence on a computing device of the medical professional. Further, theprocessing device may cause presentation, in real-time or near real-timeduring a telemedicine session with the computing device of the medicalprofessional, of the treatment plan and the billing sequence on thecomputing device of the medical professional.

In some embodiments, the processing device may control, based on thetreatment plan, the treatment apparatus 70 used by the patient toperform the treatment plan. For example, the processing device maytransmit a control signal to cause a range of motion of the pedals 102to adjust (e.g., by electromechanically adjusting the pedals 102attached to the pedal arms 104 inwardly or outwardly on the axle 106) toa setting specified in the treatment plan. In some embodiments, and asfurther described herein, a patient may view the treatment plan and/orthe billing sequence and select to initiate the treatment plan using thepatient interface 50. In some embodiments, and as further describedherein, an assistant (e.g., medical professional) may view the treatmentplan and/or the billing sequence and, using the assistant interface 94,select to initiate the treatment plan. In such an embodiment, thetreatment apparatus 70 may be distally controlled via a remote computingdevice (e.g., server 30, assistant interface 94, etc.). For example, theremote computing device may transmit one or more control signals to thecontroller 72 of the treatment apparatus 70 to cause the controller 72to execute instructions based on the control signals. By executing theinstructions, the controller 72 may control various parts (e.g., pedals,motor, etc.) of the treatment apparatus 70 in real-time or nearreal-time while the patient uses the treatment apparatus 70.

In some embodiments, the treatment plan, including the configurations,settings, range of motion settings, pain level, force settings, speedsettings, etc. of the treatment apparatus 70 for various exercises, maybe transmitted to the controller of the treatment apparatus 70. In oneexample, if the user provides an indication, via the patient interface50, that he is experiencing a high level of pain at a particular rangeof motion, the controller may receive the indication. Based on theindication, the controller may electronically adjust the range of motionof the pedal 102 by adjusting the pedal inwardly or outwardly via one ormore actuators, hydraulics, springs, electric, mechanical, optical,opticoelectric or electromechanical motors, or the like. When the userindicates certain pain levels during an exercise, the treatment plan maydefine alternative range of motion settings for the pedal 102.Accordingly, once the treatment plan is uploaded to the controller ofthe treatment apparatus 70, the treatment apparatus may beself-functioning. It should be noted that the patient (via the patientinterface 50) and/or the assistant (via the assistant interface 94) mayoverride any of the configurations or settings of the treatmentapparatus 70 at any time. For example, the patient may use the patientinterface 50 to cause the treatment apparatus 70 to immediately stop, ifso desired.

FIG. 11 shows an example embodiment of a method 1100 for receivingrequests from computing devices and modifying the billing sequence basedon the requests according to the present disclosure. Method 1100includes operations performed by processors of a computing device (e.g.,any component of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In some embodiments, one or more operations ofthe method 1100 are implemented in computer instructions stored on amemory device and executed by a processing device. The method 1100 maybe performed in the same or in a similar manner as described above inregard to method 1000. The operations of the method 1100 may beperformed in some combination with any of the operations of any of themethods described herein.

At 1102, the processing device may receive, from a computing device, afirst request pertaining to the billing sequence. The request may bereceived from a computing device of a medical professional. The requestmay specify that the medical professional desires instant payment of hisor her portion of the bills included in the billing sequence, funds tobe received sooner than had the original billing sequence beenimplemented, an optimized total amount of the funds to be received, anoptimized number of payments to be received, an optimized schedule forthe funds to be received, or some combination thereof.

At 1104, the processing device may receive, from another computingdevice of an insurance provider, a second request pertaining to thebilling sequence. The second request may specify the insurance providerdesires instant payment of their portion of the bills in the billingsequence, to be received sooner than had the original billing sequencebeen implemented, an optimized total amount of the funds to be received,an optimized number of payments to be received, an optimized schedulefor the funds to be received, or some combination thereof.

At 1006, the processing device may modify, based on the first requestand the second request, the billing sequence to generate a modifiedbilling sequence, such that the modified billing sequence results infunds being received sooner than had the original billing sequence beenimplemented, an optimized total amount of the funds to be received, anoptimized number of payments to be received, an optimized schedule forthe funds to be received, or some combination thereof. The modifiedbilling sequence may be generated to comply with the billing procedures.For example, the modified billing sequence may be generated to ensurethat the modified billing sequence is free of medical billing fraudand/or balance billing.

FIG. 12 shows an embodiment of the overview display 120 of the assistantinterface 94 presenting, in real-time during a telemedicine session,optimal treatment plans that generate certain monetary value amounts andresult in certain patient outcomes according to the present disclosure.As depicted, the overview display 120 just includes sections for thepatient profile 130 and the video feed display 180, including theself-video display 182. Any suitable configuration of controls andinterfaces of the overview display 120 described with reference to FIG.5 may be presented in addition to or instead of the patient profile 130,the video feed display 180, and the self-video display 182. In someembodiments, the same optimal treatment plans, including monetary valueamounts generated, patient outcomes, and/or risks, may be presented in adisplay screen 54 of the patient interface 50. In some embodiments, theoptimal treatment plans including monetary value amounts generated,patient outcomes, and/or risks may be presented simultaneously, inreal-time or near real-time, during a telehealth session, on both thedisplay screen 54 of the patient interface 50 and the display screen 24of the assistant interface 94.

The assistant (e.g., medical professional) using the assistant interface94 (e.g., computing device) during the telemedicine session may bepresented in the self-video 182 in a portion of the overview display 120(e.g., user interface presented on a display screen 24 of the assistantinterface 94) that also presents a video from the patient in the videofeed display 180. Further, the video feed display 180 may also include agraphical user interface (GUI) object 700 (e.g., a button) that enablesthe medical professional to share, in real-time or near real-time duringthe telemedicine session, the optimal treatment plans including themonetary value amounts generated, patient outcomes, risks, etc. with thepatient on the patient interface 50. The medical professional may selectthe GUI object 700 to share the treatment plans. As depicted, anotherportion of the overview display 120 includes the patient profile display130.

The patient profile display 130 is presenting two example optimaltreatment plans 1200 and 1202. The optimal treatment plan 1200 includesa monetary value amount generated 1204 by the optimal treatment plan1200, a patient outcome 1206 associated with performing the optimaltreatment plan 1200, and a risk 1208 associated with performing theoptimal treatment plan 1200. The optimal treatment plan 1202 includes amonetary value amount generated 1210 by the optimal treatment plan 1202,a patient outcome 1212 associated with performing the optimal treatmentplan 1202, and a risk 1214 associated with performing the optimaltreatment plan 1202. The risks may be determined using an algorithm thataccounts for a difficulty of a procedure (e.g., open heart surgeryversus an endoscopy), a skill level of a medical professional based onyears of experience, malpractice judgments, and/or peer reviews, andvarious other factors.

To generate the optimal treatment plans 1200 and 1202, the artificialintelligence engine 11 may receive (i) information pertaining to amedical condition of the patient; (ii) a set of treatment plans that,when applied to patients having a similar medical condition as thepatient, cause outcomes to be achieved by the patients; (ii) a set ofmonetary value amounts associated with the set of treatment plans;and/or (iii) a set of constraints including laws, regulations, and/orrules pertaining to billing codes associated with the set of treatmentplans (e.g., more particularly, laws, regulations, and/or rulespertaining to billing codes associated with procedures and/orinstructions included in the treatment plans).

Based on the set of treatment plans, the set of monetary value amounts,and the set of constraints, the artificial intelligence engine 11 mayuse one or more trained machine learning models 13 to generate theoptimal treatment plans 1200 and 1202 for the patient. Each of theoptimal treatment plans 1200 and 1202 complies with the set ofconstraints and represents a patient outcome and an associated monetaryvalue amount generated. It should be noted that the optimal treatmentplans may be generated and tailored based on one or more parameters(e.g., monetary value amount generated, patient outcome, and/or risk).The one or more parameters may be selected electronically by theartificial intelligence engine 11 or by a user (e.g., medicalprofessional) using a user interface (e.g., patient profile display 130)to tailor how the treatment plans are optimized. For example, the usermay specify she wants to see optimal treatment plans tailored based onthe best patient outcome or, alternatively, based on the maximummonetary value amount generated.

Each of the respective treatment plans 1200 and 1202 may include one ormore procedures to be performed on the patient based on the informationpertaining to the medical condition of the patient. Further, each of therespective treatment plans 1200 and 1202 may include one or more billingcodes associated with the one or more procedures.

For example, as depicted, the patient profile display 130 presents“Patient has Condition Z”, where condition Z may be associated withinformation of the patient including a particular medical diagnosis codereceived from an EMR system. The patient profile display 130 alsopresents the optimal treatment plan 1200, “Optimal Treatment plan 1Tailored for [Parameter X]: 1. Procedure A; billing code 123; 2.Procedure B; billing code 234”. The [Parameter X] may be any suitableparameter, such as a monetary value amount generated by the optimaltreatment plan, a patient outcome associated with performing the optimaltreatment plan, and/or a risk associated with performing the optimaltreatment plan.

The patient profile display 130 presents “Monetary Value AmountGenerated for Treatment Plan 1: $monetaryValueX”. monetaryValueX may beany suitable monetary value amount associated with the optimal treatmentplan 1200. In some embodiments, monetaryValueX may be a configurableparameter that enables the user to set a desired monetary value amountto be generated.

The patient profile display 130 presents “Patient Outcome:patientOutcome1”. patientOutcome1 may be any suitable patient outcome(e.g., full recovery or partial recovery, achievement of full orpartial: desired range of motion, flexibility, strength, or pliability,etc.) associated with the optimal treatment plan 1200. In someembodiments, patientOutcomel may be a configurable parameter thatenables the user to set a desired patient outcome that results fromperforming the optimal treatment plan.

The patient profile display 130 presents “Risk: risk1”. risk1 may be anysuitable risk (e.g., low, medium, or high; or an absolute or relativenumber or magnitude on a scale; etc.) associated with the optimaltreatment plan 1200. In some embodiments, risk1 may be a configurableparameter that enables the user to set a desired risk associated withperforming the optimal treatment plan.

Further, the patient profile display 130 also presents the optimaltreatment plan 1202, “Optimal Treatment plan 2 Tailored for [ParameterY]: 1. Procedure A; billing code 123; 2. Procedure C; billing code 345”.The [Parameter Y] may be any suitable parameter, such as a monetaryvalue amount generated by the optimal treatment plan, a patient outcomeassociated with performing the optimal treatment plan, and/or a riskassociated with performing the optimal treatment plan.

The patient profile display 130 presents “Monetary Value AmountGenerated for Treatment Plan 1: $monetaryValueY”. monetaryValueX may beany suitable monetary value amount associated with the optimal treatmentplan 1202. In some embodiments, monetaryValueX may be a configurableparameter that enables the user to set a desired monetary value amountto be generated.

The patient profile display 130 presents “Patient Outcome:patientOutcome2”. patientOutcome2 may be any suitable patient outcome(e.g., full recovery or partial recovery, achievement of full orpartial: desired range of motion, flexibility, strength, or pliability,etc.) associated with the optimal treatment plan 1202. In someembodiments, patientOutcome2 may be a configurable parameter thatenables the user to set a desired patient outcome that results fromperforming the optimal treatment plan.

The patient profile display 130 presents “Risk: risk2”. Risk2 may be anysuitable risk (e.g., low, medium, or high; or an absolute or relativenumber or magnitude on a scale; etc.) associated with the optimaltreatment plan 1200. In some embodiments, risk2 may be a configurableparameter that enables the user to set a desired risk associated withperforming the optimal treatment plan.

In the depicted example, the [Parameter X] and the [Parameter Y] bothcorrespond to the parameter pertaining to the monetary value amountgenerated. The monetary value amount generated for [Parameter X] may beset higher than the monetary value amount generated for [Parameter Y].Accordingly, the optimal treatment plan 1200 may include differentprocedures (e.g., Procedure A and Procedure B) that result in the highermonetary amount generated ([Parameter X]), a better outcome (e.g.,patientOutcome1), and a higher risk (e.g., risk1) than the optimaltreatment plan 1202, which may result in a lesser monetary value amountgenerated ([Parameter y]), less desirable outcome (e.g.,patientOutcome2), and a lower risk (e.g., risk2).

Further, as depicted, a graphical element (e.g., button for “SELECT”)may be presented in the patient profile display 130. Although just onegraphical element is presented, any suitable number of graphicalelements for selecting an optimal treatment may be presented in thepatient profile display 130. As depicted, a user (e.g., medicalprofessional or patient) uses an input peripheral (e.g., mouse,keyboard, microphone, touchscreen) to select (as represented by circle1250) the graphical element associated with the optimal treatment plan1200. The medical professional may prefer to receive a higher monetaryvalue amount generated (e.g., [Parameter X]) from the optimal treatmentplan and/or the patient may have requested the best patient outcomepossible. Accordingly, the assistant interface 94 may transmit a controlsignal to the treatment apparatus 70 to control, based on the treatmentplan 1200, operation of the treatment apparatus 70. In some embodiments,the patient may select the treatment plan from the display screen 54 andthe patient interface 50 may transmit a control signal to the treatmentapparatus 70 to control, based on the selected treatment plan 1200,operation of the treatment apparatus 70.

It should be noted that, in some embodiments, just treatment plans thatpass muster with respect to standard of care, regulations, laws, and thelike may be presented as viable options on a computing device of thepatient and/or the medical professional. Accordingly, non-viabletreatment plans that fail to meet a standard of care, violate aregulation and/or law, etc. may not be presented as options forselection. For example, the non-viable treatment plan options may befiltered from a result set presented on the computing device. In someembodiments, any treatment plan (e.g., both viable and non-viableoptions) may be presented on the computing device of the patient and/ormedical professional.

FIG. 13 shows an example embodiment of a method 1300 for generatingoptimal treatment plans for a patient, where the generating is based ona set of treatment plans, a set of monetary value amounts, and a set ofconstraints according to the present disclosure. Method 1300 includesoperations performed by processors of a computing device (e.g., anycomponent of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In some embodiments, one or more operations ofthe method 1300 are implemented in computer instructions stored on amemory device and executed by a processing device. The method 1300 maybe performed in the same or in a similar manner as described above inregard to method 1300. The operations of the method 1300 may beperformed in some combination with any of the operations of any of themethods described herein.

Prior to the method 1300 beginning, the processing device may receiveinformation pertaining to the patient. The information may include amedical diagnosis code and/or the various characteristics (e.g.,personal information, performance information, and measurementinformation, etc.) described herein. The processing device may match theinformation of the patient with similar information from other patients.Based upon the matching, the processing device may select a set oftreatment plans that cause certain outcomes (e.g., desired results) tobe achieved by the patients.

At 1302, the processing device may receive the set of treatment plansthat, when applied to patients, cause outcomes to be achieved by thepatients. In some embodiments, the set of treatment plans may specifyprocedures to perform for the condition of the patient, a set ofexercises to be performed by the patient using the treatment apparatus70, a periodicity to perform the set of exercises using the treatmentapparatus 70, a frequency to perform the set of exercises using thetreatment apparatus 70, settings and/or configurations for portions(e.g., pedals, seat, etc.) of the treatment apparatus 70, and the like.

At 1304, the processing device may receive a set of monetary valueamounts associated with the set of treatment plans. A respectivemonetary value amount of the set of monetary value amounts may beassociated with a respective treatment plan of the set of treatmentplans. For example, one respective monetary value amount may indicate$5,000 may be generated if the patient performs the respective treatmentplan (e.g., including a consultation with a medical professional duringa telemedicine session, rental fee for the treatment apparatus 70,follow-up in-person visit with the medical professional, etc.).

At 1306, the processing device may receive a set of constraints. The setof constraints may include rules pertaining to billing codes associatedwith the set of treatment plans. In some embodiments, the processingdevice may receive a set of billing codes associated with the proceduresto be performed for the patient, the set of exercises, etc. and applythe set of billing codes to the treatment plans in view of the rules. Insome embodiments, the set of constraints may further include constraintsset forth in regulations, laws, or some combination thereof. Forexample, the laws and/or regulations may specify that certain billingcodes (e.g., DRG or ICD-10) be used for certain procedures and/orexercises.

At 1308, the processing device may generate, by the artificialintelligence engine 11, optimal treatment plans for a patient.Generating the optimal treatment plans may be based on the set oftreatment plans, the set of monetary value amounts, and the set ofconstraints. In some embodiments, generating the optimal treatment plansmay include optimizing the optimal treatment plans for fees, revenue,profit (e.g., gross, net, etc.), earnings before interest (EBIT),earnings before interest, depreciation and amortization (EBITDA), cashflow, free cash flow, working capital, gross revenue, a value ofwarrants, options, equity, debt, derivatives or any other financialinstrument, any generally acceptable financial measure or metric incorporate finance or according to Generally Accepted AccountingPrinciples (GAAP) or foreign counterparts, or some combination thereof.

Each of the optimal treatment plans complies with the set of constraintsand represents a patient outcome and an associated monetary value amountgenerated. To ensure the procedure is allowed, the set of constraintsmay be enforced by comparing each procedure included in the optimaltreatment plan with the set of constraints. If the procedure is allowed,based on the set of constraints, the procedure is included in theoptimal treatment plan. If the procedure is not allowed, based on theset of constraints, the procedure is excluded from the optimal treatmentplan. The optimal treatment plans may pertain to habilitation,prehabilitation, rehabilitation, post-habilitation, exercise, strength,pliability, flexibility, weight stability, weight gain, weight loss,cardiovascular fitness, performance or metrics, endurance, respiratoryfitness, performance or metrics, or some combination thereof.

In some embodiments, a first optimal treatment plan of the optimaltreatment plans may result in a first patient outcome and a firstmonetary value amount generated, and a second optimal treatment plan ofthe optimal treatment plans may result in a second patient outcome and asecond monetary value amount generating. The second patient outcome maybe better than the first patient outcome and the second monetary valueamount generated may be greater than the first monetary value amountgenerated. Based on certain criteria (e.g., whether the patient desiresthe best patient outcome or has limited funds), either the first orsecond optimal treatment plan may be selected and implemented to controlthe treatment apparatus 70. In this and other scenarios herein, bothpatient outcomes, even the inferior one, are at or above the standard ofcare dictated by ethical medical practices for individual medicalprofessionals, hospitals, etc., as the case may be, and such standard ofcare shall further be consistent with applicable governing regulationsand laws, whether de facto or de jure.

At 1310, the processing device may transmit, in real-time or nearreal-time, the optimal treatment plans to be presented on a computingdevice of a medical professional. The optimal treatment plans may bepresented on the computing device of the medical professional during atelemedicine or telehealth session in which a computing device of thepatient is engaged. In some embodiments, the processing device maytransmit the optimal treatment plans to be presented, in real-time ornear real-time, on a computing device of the patient during atelemedicine session in which the computing device of the medicalprofessional is engaged.

In some embodiments, the processing device may receive levels of riskassociated with the set of treatment plans. In some embodiments, thelevels of risk may be preconfigured for each of the set of treatmentplans. In some embodiments, the levels of risk may be dynamicallydetermined based on a number of factors (e.g., condition of the patient,difficulty of procedures included in the treatment plan, etc.). In someembodiments, generating the optimal treatment plans may also be based onthe levels of risk. Further, in some embodiments, the processing devicemay transmit the optimal treatment plans and the levels of risk to bepresented on the computing device of the medical professional. As usedherein, “levels of risk” includes levels of risk for each of one or morerisks.

FIG. 14 shows an example embodiment of a method 1400 for receiving aselection of a monetary value amount and generating an optimal treatmentplan based on a set of treatment plans, the monetary value amount, and aset of constraints according to the present disclosure. Method 1400includes operations performed by processors of a computing device (e.g.,any component of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In some embodiments, one or more operations ofthe method 1400 are implemented in computer instructions stored on amemory device and executed by a processing device. The method 1400 maybe performed in the same or a similar manner as described above inregard to method 1000. The operations of the method 1400 may beperformed in some combination with any of the operations of any of themethods described herein.

At 1402, the processing device may receive a selection of a certainmonetary value amount of the set of monetary value amounts. For example,a graphical element included on a user interface of a computing devicemay enable a user to select (e.g., enter a monetary value amount in atextbox or select from a drop-down list, radio button, scrollbar, etc.)the certain monetary value amount to be generated by an optimaltreatment plan. The certain monetary value amount may be transmitted tothe artificial intelligence engine 11, which uses the certain monetaryvalue amount to generate an optimal treatment plan tailored for thedesired monetary value amount.

At 1404, the processing device may generate, by the artificialintelligence engine 11, an optimal treatment plan based on the set oftreatment plans, the certain monetary value amount, and the set ofconstraints. The optimal treatment plan complies with the set ofconstraints and represents another patient outcome and the certainmonetary value amount.

FIG. 15 shows an example embodiment of a method 1500 for receiving aselection of an optimal treatment plan and controlling, based on theoptimal treatment plan, a treatment apparatus while the patient uses thetreatment apparatus according to the present disclosure. Method 1500includes operations performed by processors of a computing device (e.g.,any component of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In some embodiments, one or more operations ofthe method 1500 are implemented in computer instructions stored on amemory device and executed by a processing device. The method 1500 maybe performed in the same or a similar manner as described above inregard to method 1000. The operations of the method 1500 may beperformed in some combination with any of the operations of any of themethods described herein.

Prior to the method 1500 being executed, various optimal treatment plansmay be generated by one or more trained machine learning models 13 ofthe artificial intelligence engine 11. For example, based on a set oftreatment plans pertaining to a medical condition of a patient, a set ofmonetary value amounts associated with the set of treatment plans, and aset of constraints, the one or more trained machine learning models 13may generate the optimal treatment plans. In some embodiments, the oneor more trained machine learning models 13 may generate a billingsequence that is tailored based on a parameter (e.g., a fee to be paidto a medical professional, a payment plan for the patient to pay off anamount of money owed, a plan of reimbursement, a monetary value amountto be paid to an insurance provider, or some combination thereof). Thevarious treatment plans and/or billing sequences may be transmitted toone or computing devices of a patient and/or medical professional.

At 1502 of the method 1500, the processing device may receive aselection of an optimal treatment plan from the optimal treatment plans.The selection may have been entered on a user interface presenting theoptimal treatment plans on the patient interface 50 and/or the assistantinterface 94. In some embodiments, the processing device may receive aselection of a billing sequence associated with at least a portion of atreatment plan. The selection may have been entered on a user interfacepresenting the billing sequence on the patient interface 50 and/or theassistant interface 94. If the user selects a particular billingsequence, the treatment plan associated with the selected billingsequence may be selected.

At 1504, the processing device may control, based on the selectedoptimal treatment plan, the treatment apparatus 70 while the patientuses the treatment apparatus. In some embodiments, the controlling isperformed distally by the server 30. For example, if the selection ismade using the patient interface 50, one or more control signals may betransmitted from the patient interface 50 to the treatment apparatus 70to configure, according to the selected treatment plan, a setting of thetreatment apparatus 70 to control operation of the treatment apparatus70. Further, if the selection is made using the assistant interface 94,one or more control signals may be transmitted from the assistantinterface 94 to the treatment apparatus 70 to configure, according tothe selected treatment plan, a setting of the treatment apparatus 70 tocontrol operation of the treatment apparatus 70.

It should be noted that, as the patient uses the treatment apparatus 70,the sensors 76 may transmit measurement data to a processing device. Theprocessing device may dynamically control, according to the treatmentplan, the treatment apparatus 70 by modifying, based on the sensormeasurements, a setting of the treatment apparatus 70. For example, ifthe force measured by the sensor 76 indicates the user is not applyingenough force to a pedal 102, the treatment plan may indicate to reducethe required amount of force for an exercise.

It should be noted that, as the patient uses the treatment apparatus 70,the user may use the patient interface 50 to enter input pertaining to apain level experienced by the patient as the patient performs thetreatment plan. For example, the user may enter a high degree of painwhile pedaling with the pedals 102 set to a certain range of motion onthe treatment apparatus 70. The pain level may cause the range of motionto be dynamically adjusted based on the treatment plan. For example, thetreatment plan may specify alternative range of motion settings if acertain pain level is indicated when the user is performing an exerciseat a certain range of motion.

Different people have different tolerances for pain. In someembodiments, a person may indicate a pain level they are willing totolerate to achieve a certain result (e.g., a certain range of motionwithin a certain time period). A high degree of pain may be acceptableto a person if that degree of pain is associated with achieving thecertain result. The treatment plan may be tailored based on theindicated pain level. For example, the treatment plan may includecertain exercises, frequencies of exercises, and/or periodicities ofexercises that are associated with the indicated pain level and desiredresult for people having characteristics similar to characteristics ofthe person.

FIG. 16 shows an example computer system 1600 which can perform any oneor more of the methods described herein, in accordance with one or moreaspects of the present disclosure. In one example, computer system 1600may include a computing device and correspond to the assistanceinterface 94, reporting interface 92, supervisory interface 90,clinician interface 20, server 30 (including the AI engine 11), patientinterface 50, ambulatory sensor 82, goniometer 84, treatment apparatus70, pressure sensor 86, or any suitable component of FIG. 1. Thecomputer system 1600 may be capable of executing instructionsimplementing the one or more machine learning models 13 of theartificial intelligence engine 11 of FIG. 1. The computer system may beconnected (e.g., networked) to other computer systems in a LAN, anintranet, an extranet, or the Internet, including via the cloud or apeer-to-peer network. The computer system may operate in the capacity ofa server in a client-server network environment. The computer system maybe a personal computer (PC), a tablet computer, a wearable (e.g.,wristband), a set-top box (STB), a personal Digital Assistant (PDA), amobile phone, a camera, a video camera, an Internet of Things (IoT)device, or any device capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatdevice. Further, while only a single computer system is illustrated, theterm “computer” shall also be taken to include any collection ofcomputers that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methods discussedherein.

The computer system 1600 includes a processing device 1602, a mainmemory 1604 (e.g., read-only memory (ROM), flash memory, solid statedrives (SSDs), dynamic random access memory (DRAM) such as synchronousDRAM (SDRAM)), a static memory 1606 (e.g., flash memory, solid statedrives (SSDs), static random access memory (SRAM)), and a data storagedevice 1608, which communicate with each other via a bus 1610.

Processing device 1602 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1602 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1402 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a system on a chip, a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 1402 is configured to execute instructions forperforming any of the operations and steps discussed herein.

The computer system 1600 may further include a network interface device1612. The computer system 1600 also may include a video display 1614(e.g., a liquid crystal display (LCD), a light-emitting diode (LED), anorganic light-emitting diode (OLED), a quantum LED, a cathode ray tube(CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), oneor more input devices 1616 (e.g., a keyboard and/or a mouse or agaming-like control), and one or more speakers 1618 (e.g., a speaker).In one illustrative example, the video display 1614 and the inputdevice(s) 1616 may be combined into a single component or device (e.g.,an LCD touch screen).

The data storage device 1616 may include a computer-readable medium 1620on which the instructions 1622 embodying any one or more of the methods,operations, or functions described herein is stored. The instructions1622 may also reside, completely or at least partially, within the mainmemory 1604 and/or within the processing device 1602 during executionthereof by the computer system 1600. As such, the main memory 1604 andthe processing device 1602 also constitute computer-readable media. Theinstructions 1622 may further be transmitted or received over a networkvia the network interface device 1612.

While the computer-readable storage medium 1620 is shown in theillustrative examples to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

Clause 1. A method for generating, by an artificial intelligence engine,a treatment plan and a billing sequence associated with the treatmentplan, the method comprising:

receiving information pertaining to a patient, wherein the informationcomprises a medical diagnosis code of the patient;

generating, based on the information, the treatment plan for thepatient, wherein the treatment plan comprises a plurality ofinstructions for the patient to follow;

receiving a set of billing procedures associated with the plurality ofinstructions, wherein the set of billing procedures comprises rulespertaining to billing codes, timing, constraints, or some combinationthereof;

generating, based on the set of billing procedures, the billing sequencefor at least a portion of the plurality of instructions, wherein thebilling sequence is tailored according to a certain parameter; and

transmitting the treatment plan and the billing sequence to a computingdevice.

Clause 2. The method of any preceding clause, further comprisingdistally controlling, based on the treatment plan, a treatment apparatusused by the patient to perform the treatment plan.

Clause 3. The method of any preceding clause, wherein the certainparameter is a fee to be paid to a medical professional, a payment planfor the patient to pay off an amount of money owed, a plan ofreimbursement, an amount of revenue to be paid to an insurance provider,or some combination thereof.

Clause 4. The method of any preceding clause, wherein the treatment planis for habilitation, pre-habilitation, rehabilitation,post-habilitation, exercise, strength training, endurance training,weight loss, weight gain, flexibility, pliability, or some combinationthereof.

Clause 5. The method of any preceding clause, wherein the plurality ofinstructions comprises:

a plurality of exercises for the patient to perform,

an order for the plurality of exercises,

a frequency for performing the plurality of exercises,

a diet regimen,

a sleep regimen,

a plurality of procedures to perform on the patient,

an order for the plurality of procedures,

a medication regimen,

a plurality of sessions for the patient, or

some combination thereof.

Clause 6. The method of any preceding clause, further comprising causingpresentation, in real-time or near real-time during a telemedicinesession with another computing device of the patient, of the treatmentplan and the billing sequence on the computing device of a medicalprofessional.

Clause 7. The method of any preceding clause, further comprising:

receiving, from the computing device, a first request pertaining to thebilling sequence;

receiving, from another computing device of an insurance provider, asecond request pertaining to the billing sequence;

modifying, based on the first request and the second request, thebilling sequence to generate a modified billing sequence, such that themodified billing sequence results in funds being received sooner thanhad the billing sequence been implemented, an optimized total amount ofthe funds being received, an optimized number of payments beingreceived, an optimized schedule for the funds being received, or somecombination thereof.

Clause 8. The method of any preceding clause, wherein the constraintsfurther comprise constraints set forth in regulations, laws, or somecombination thereof.

Clause 9. The method of any preceding clause, further comprisingtransmitting the treatment plan and the billing sequence to be presentedon a second computing device of the patient in real-time or nearreal-time during a telemedicine session in which the computing device ofthe medical professional is engaged.

Clause 10. A system, comprising:

a memory device storing instructions;

a processing device communicatively coupled to the memory device, theprocessing device executes the instructions to:

receive information pertaining to a patient, wherein the informationcomprises a medical diagnosis code of the patient;

generate, based on the information, a treatment plan for the patient,wherein the treatment plan comprises a plurality of instructions for thepatient to follow;

receive a set of billing procedures associated with the plurality ofinstructions, wherein the set of billing procedures comprises rulespertaining to billing codes, timing, constraints, or some combinationthereof;

generate, based on the set of billing procedures, a billing sequence forat least a portion of the plurality of instructions, wherein the billingsequence is tailored according to a certain parameter; and

transmit the treatment plan and the billing sequence to a computingdevice.

Clause 11. The system of any preceding clause, wherein the processingdevice is further to distally control, based on the treatment plan, atreatment apparatus used by the patient to perform the treatment plan.

Clause 12. The system of any preceding clause, wherein the certainparameter is a fee to be paid to a medical professional, a payment planfor the patient to pay off an amount of money owed, a plan ofreimbursement, an amount of revenue to be paid to an insurance provider,or some combination thereof.

Clause 13. The system of any preceding clause, wherein the treatmentplan is for habilitation, pre-habilitation, rehabilitation,post-habilitation, exercise, strength training, endurance training,weight loss, weight gain, flexibility, pliability, or some combinationthereof.

Clause 14. The system of any preceding clause, wherein the plurality ofinstructions comprises:

a plurality of exercises for the patient to perform,

an order for the plurality of exercises,

a frequency for performing the plurality of exercises,

a diet regimen,

a sleep regimen,

a plurality of procedures to perform on the patient,

an order for the plurality of procedures,

a medication regimen,

a plurality of sessions for the patient, or

some combination thereof.

Clause 15. The system of any preceding clause, wherein the processingdevice is further to cause presentation, in real-time or near real-timeduring a telemedicine session with another computing device of thepatient, of the treatment plan and the billing sequence on the computingdevice of a medical professional.

Clause 16. The system of any preceding clause, wherein the processingdevice is further to:

receive, from the computing device, a first request pertaining to thebilling sequence;

receive, from another computing device of an insurance provider, asecond request pertaining to the billing sequence;

modify, based on the first request and the second request, the billingsequence to generate a modified billing sequence, such that the modifiedbilling sequence results in funds being received sooner than had thebilling sequence been implemented, an optimized total amount of thefunds being received, an optimized number of payments being received, anoptimized schedule for the funds being received, or some combinationthereof.

Clause 17. The system of any preceding clause, wherein the constraintsfurther comprise constraints set forth in regulations, laws, or somecombination thereof.

Clause 18. The system of any preceding clause, wherein the processingdevice is further to transmit the treatment plan and the billingsequence to be presented on a second computing device of the patient inreal-time or near real-time during a telemedicine session in which thecomputing device of the medical professional is engaged.

Clause 19. A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

receive information pertaining to a patient, wherein the informationcomprises a medical diagnosis code of the patient;

generate, based on the information, a treatment plan for the patient,wherein the treatment plan comprises a plurality of instructions for thepatient to follow;

receive a set of billing procedures associated with the plurality ofinstructions, wherein the set of billing procedures comprises rulespertaining to billing codes, timing, constraints, or some combinationthereof;

generate, based on the set of billing procedures, a billing sequence forat least a portion of the plurality of instructions, wherein the billingsequence is tailored according to a certain parameter; and

transmit the treatment plan and the billing sequence to a computingdevice.

Clause 20. The computer-readable medium of any preceding clause, whereinthe processing device is further to distally control, based on thetreatment plan, a treatment apparatus used by the patient to perform thetreatment plan.

Clause 21. A method for generating, by an artificial intelligenceengine, treatment plans for optimizing patient outcome and monetaryvalue amount generated, the method comprising:

receiving a set of treatment plans that, when applied to patients, causeoutcomes to be achieved by the patients;

receiving a set of monetary value amounts associated with the set oftreatment plans, wherein a respective monetary value amount of the setof monetary value amounts is associated with a respective treatment planof the set of treatment plans;

receiving a set of constraints, wherein the set of constraints comprisesrules pertaining to billing codes associated with the set of treatmentplans;

generating, by the artificial intelligence engine, optimal treatmentplans for a patient, wherein the generating is based on the set oftreatment plans, the set of monetary value amounts, and the set ofconstraints, wherein each of the optimal treatment plans complies withthe set of constraints and represents a patient outcome and anassociated monetary value amount generated; and

transmitting the optimal treatment plans to be presented on a computingdevice.

Clause 22. The method of any preceding clause, wherein the optimaltreatment plans are for habilitation, pre-habilitation, rehabilitation,post-habilitation, exercise, strength, pliability, flexibility, weightstability, weight gain, weight loss, cardiovascular, endurance,respiratory, or some combination thereof.

Clause 23. The method of any preceding clause, further comprising:

receiving a selection of a certain monetary value amount of the set ofmonetary value amounts; and

generating, by the artificial intelligence engine, an optimal treatmentplan based on the set of treatment plans, the certain monetary valueamount, and the set of constraints, wherein the optimal treatment plancomplies with the set of constraints and represents another patientoutcome and the certain monetary value amount.

Clause 24. The method of any preceding clause, wherein the set oftreatment plans specifies a set of exercises to be performed by thepatient using a treatment apparatus, and the method further comprises:

receiving a set of billing codes associated with the set of exercises;and

correlating the set of billing codes with the rules.

Clause 25. The method of any preceding clause, further comprising:

receiving levels of risk associated with the set of treatment plans,wherein the generating the optimal treatment plans is also based on thelevels of risk; and

transmitting the optimal treatment plans and the level of risks to bepresented on the computing device of the medical professional.

Clause 26. The method of any preceding clause, wherein:

a first optimal treatment plan of the optimal treatment plans results ina first patient outcome and a first monetary value amount generated; and

a second optimal treatment plan of the optimal treatment plans resultsin a second patient outcome and a second monetary value amountgenerated, wherein the second patient outcome is better than the firstpatient outcome and the second revenue value generated is greater thanthe first monetary value amount generated.

Clause 27. The method of any preceding clause, wherein the set ofconstraints further comprises constraints set forth in regulations,laws, or some combination thereof.

Clause 28. The method of any preceding clause, further comprisingtransmitting the optimal treatment plans to be presented on a computingdevice of the patient in real-time or near real-time during atelemedicine session in which the computing device of the medicalprofessional is engaged.

Clause 29. The method of any preceding clause, further comprising:

receiving a selection of an optimal treatment plan from the optimaltreatment plans; and

controlling, based on the optimal treatment plan, a treatment apparatuswhile the patient uses the treatment apparatus.

Clause 30. The method of any preceding clause, wherein the controllingis performed distally.

Clause 31. The method of any preceding clause, wherein:

the optimal treatment plans are presented on the computing device of amedical professional during a telemedicine session in which a computingdevice of the patient is engaged.

Clause 32. The method of any preceding clause, wherein:

the optimal treatment plans are presented on the computing device ofpatient during a telemedicine session in which a computing device of amedical professional is engaged.

Clause 33. The method of any preceding clause, wherein the generating,based on the set of treatment plans, the set of monetary value amounts,and the set of constraints, the optimal treatment plans furthercomprises optimizing the optimal treatment plans for revenue generated,profit generated, cash flow generated, free cash flow generated, grossrevenue generated, earnings before interest taxes amortization (EBITA)generated, or some combination thereof.

Clause 34. A system, comprising:

a memory device storing instructions; and

a processing device communicatively coupled to the memory device, theprocessing device executes the instructions to:

receive a set of treatment plans that, when applied to patients, causeoutcomes to be achieved by the patients;

receive a set of monetary value amounts associated with the set oftreatment plans, wherein a respective monetary value amount of the setof monetary value amounts is associated with a respective treatment planof the set of treatment plans;

receive a set of constraints, wherein the set of constraints comprisesrules pertaining to billing codes associated with the set of treatmentplans;

generate, by an artificial intelligence engine, optimal treatment plansfor a patient, wherein the generating is based on the set of treatmentplans, the set of monetary value amounts, and the set of constraints,wherein each of the optimal treatment plans complies with the set ofconstraints and represents a patient outcome and an associated monetaryvalue amount generated; and

transmit the optimal treatment plans to be presented on a computingdevice.

Clause 35. The system of any preceding clause, wherein the optimaltreatment plans are for habilitation, pre-habilitation, rehabilitation,post-habilitation, exercise, strength, pliability, flexibility, weightstability, weight gain, weight loss, cardiovascular, endurance,respiratory, or some combination thereof.

Clause 36. The system of any preceding clause, wherein the processingdevice is further to:

receive a selection of a certain monetary value amount of the set ofmonetary value amounts; and

generate, by the artificial intelligence engine, an optimal treatmentplan based on the set of treatment plans, the certain monetary valueamount, and the set of constraints, wherein the optimal treatment plancomplies with the set of constraints and represents another patientoutcome and the certain monetary value amount.

Clause 37. The system of any preceding clause, wherein the set oftreatment plans specifies a set of exercises to be performed by thepatient using a treatment apparatus, and the processing device isfurther to:

receive a set of billing codes associated with the set of exercises; and

correlate the set of billing codes with the rules.

Clause 38. The system of any preceding clause, wherein the processingdevice is further to:

receive levels of risk associated with the set of treatment plans,wherein the generating the optimal treatment plans is also based on thelevels of risk; and

transmit the optimal treatment plans and the level of risks to bepresented on the computing device of the medical professional.

Clause 39. The system of any preceding clause, wherein:

a first optimal treatment plan of the optimal treatment plans results ina first patient outcome and a first monetary value amount generated; and

a second optimal treatment plan of the optimal treatment plans resultsin a second patient outcome and a second monetary value amountgenerated, wherein the second patient outcome is better than the firstpatient outcome and the second revenue value generated is greater thanthe first monetary value amount generated.

Clause 40. A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

receive a set of treatment plans that, when applied to patients, causeoutcomes to be achieved by the patients;

receive a set of monetary value amounts associated with the set oftreatment plans, wherein a respective monetary value amount of the setof monetary value amounts is associated with a respective treatment planof the set of treatment plans;

receive a set of constraints, wherein the set of constraints comprisesrules pertaining to billing codes associated with the set of treatmentplans;

generate, by an artificial intelligence engine, optimal treatment plansfor a patient, wherein the generating is based on the set of treatmentplans, the set of monetary value amounts, and the set of constraints,wherein each of the optimal treatment plans complies with the set ofconstraints and represents a patient outcome and an associated monetaryvalue amount generated; and

transmit the optimal treatment plans to be presented on a computingdevice.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present disclosure. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

The various aspects, embodiments, implementations, or features of thedescribed embodiments can be used separately or in any combination. Theembodiments disclosed herein are modular in nature and can be used inconjunction with or coupled to other embodiments.

Consistent with the above disclosure, the examples of assembliesenumerated in the following clauses are specifically contemplated andare intended as a non-limiting set of examples.

What is claimed is:
 1. A method for generating, by an artificialintelligence engine, treatment plans for optimizing patient outcome andmonetary value amount generated, the method comprising: receiving a setof treatment plans that, when applied to patients, cause outcomes to beachieved by the patients; receiving a set of monetary value amountsassociated with the set of treatment plans, wherein a respectivemonetary value amount of the set of monetary value amounts is associatedwith a respective treatment plan of the set of treatment plans;receiving a set of constraints, wherein the set of constraints comprisesrules pertaining to billing codes associated with the set of treatmentplans; generating, by the artificial intelligence engine, optimaltreatment plans for a patient, wherein the generating is based on theset of treatment plans, the set of monetary value amounts, and the setof constraints, wherein each of the optimal treatment plans complieswith the set of constraints and represents a patient outcome and anassociated monetary value amount generated; and transmitting the optimaltreatment plans to be presented on a computing device.
 2. The method ofclaim 1, wherein the optimal treatment plans are for habilitation,pre-habilitation, rehabilitation, post-habilitation, exercise, strength,pliability, flexibility, weight stability, weight gain, weight loss,cardiovascular, endurance, respiratory, or some combination thereof. 3.The method of claim 1, further comprising: receiving a selection of acertain monetary value amount of the set of monetary value amounts; andgenerating, by the artificial intelligence engine, an optimal treatmentplan based on the set of treatment plans, the certain monetary valueamount, and the set of constraints, wherein the optimal treatment plancomplies with the set of constraints and represents another patientoutcome and the certain monetary value amount.
 4. The method of claim 1,wherein the set of treatment plans specifies a set of exercises to beperformed by the patient using a treatment apparatus, and the methodfurther comprises: receiving a set of billing codes associated with theset of exercises; and correlating the set of billing codes with therules.
 5. The method of claim 1, further comprising: receiving levels ofrisk associated with the set of treatment plans, wherein the generatingthe optimal treatment plans is also based on the levels of risk; andtransmitting the optimal treatment plans and the level of risks to bepresented on the computing device of the medical professional.
 6. Themethod of claim 1, wherein: a first optimal treatment plan of theoptimal treatment plans results in a first patient outcome and a firstmonetary value amount generated; and a second optimal treatment plan ofthe optimal treatment plans results in a second patient outcome and asecond monetary value amount generated, wherein the second patientoutcome is better than the first patient outcome and the second revenuevalue generated is greater than the first monetary value amountgenerated.
 7. The method of claim 1, wherein the set of constraintsfurther comprises constraints set forth in regulations, laws, or somecombination thereof.
 8. The method of claim 1, further comprisingtransmitting the optimal treatment plans to be presented on a computingdevice of the patient in real-time or near real-time during atelemedicine session in which the computing device of the medicalprofessional is engaged.
 9. The method of claim 1, further comprising:receiving a selection of an optimal treatment plan from the optimaltreatment plans; and controlling, based on the optimal treatment plan, atreatment apparatus while the patient uses the treatment apparatus. 10.The method of claim 9, wherein the controlling is performed distally.11. The method of claim 1, wherein: the optimal treatment plans arepresented on the computing device of a medical professional during atelemedicine session in which a computing device of the patient isengaged.
 12. The method of claim 1, wherein: the optimal treatment plansare presented on the computing device of patient during a telemedicinesession in which a computing device of a medical professional isengaged.
 13. The method of claim 1, wherein the generating, based on theset of treatment plans, the set of monetary value amounts, and the setof constraints, the optimal treatment plans further comprises optimizingthe optimal treatment plans for revenue generated, profit generated,cash flow generated, free cash flow generated, gross revenue generated,earnings before interest taxes amortization (EBITA) generated, or somecombination thereof.
 14. A system, comprising: a memory device storinginstructions; and a processing device communicatively coupled to thememory device, the processing device executes the instructions to:receive a set of treatment plans that, when applied to patients, causeoutcomes to be achieved by the patients; receive a set of monetary valueamounts associated with the set of treatment plans, wherein a respectivemonetary value amount of the set of monetary value amounts is associatedwith a respective treatment plan of the set of treatment plans; receivea set of constraints, wherein the set of constraints comprises rulespertaining to billing codes associated with the set of treatment plans;generate, by an artificial intelligence engine, optimal treatment plansfor a patient, wherein the generating is based on the set of treatmentplans, the set of monetary value amounts, and the set of constraints,wherein each of the optimal treatment plans complies with the set ofconstraints and represents a patient outcome and an associated monetaryvalue amount generated; and transmit the optimal treatment plans to bepresented on a computing device.
 15. The system of claim 14, wherein theoptimal treatment plans are for habilitation, pre-habilitation,rehabilitation, post-habilitation, exercise, strength, pliability,flexibility, weight stability, weight gain, weight loss, cardiovascular,endurance, respiratory, or some combination thereof.
 16. The system ofclaim 14, wherein the processing device is further to: receive aselection of a certain monetary value amount of the set of monetaryvalue amounts; and generate, by the artificial intelligence engine, anoptimal treatment plan based on the set of treatment plans, the certainmonetary value amount, and the set of constraints, wherein the optimaltreatment plan complies with the set of constraints and representsanother patient outcome and the certain monetary value amount.
 17. Thesystem of claim 14, wherein the set of treatment plans specifies a setof exercises to be performed by the patient using a treatment apparatus,and the processing device is further to: receive a set of billing codesassociated with the set of exercises; and correlate the set of billingcodes with the rules.
 18. The system of claim 14, wherein the processingdevice is further to: receive levels of risk associated with the set oftreatment plans, wherein the generating the optimal treatment plans isalso based on the levels of risk; and transmit the optimal treatmentplans and the level of risks to be presented on the computing device ofthe medical professional.
 19. The system of claim 14, wherein: a firstoptimal treatment plan of the optimal treatment plans results in a firstpatient outcome and a first monetary value amount generated; and asecond optimal treatment plan of the optimal treatment plans results ina second patient outcome and a second monetary value amount generated,wherein the second patient outcome is better than the first patientoutcome and the second revenue value generated is greater than the firstmonetary value amount generated.
 20. A tangible, non-transitorycomputer-readable medium storing instructions that, when executed, causea processing device to: receive a set of treatment plans that, whenapplied to patients, cause outcomes to be achieved by the patients;receive a set of monetary value amounts associated with the set oftreatment plans, wherein a respective monetary value amount of the setof monetary value amounts is associated with a respective treatment planof the set of treatment plans; receive a set of constraints, wherein theset of constraints comprises rules pertaining to billing codesassociated with the set of treatment plans; generate, by an artificialintelligence engine, optimal treatment plans for a patient, wherein thegenerating is based on the set of treatment plans, the set of monetaryvalue amounts, and the set of constraints, wherein each of the optimaltreatment plans complies with the set of constraints and represents apatient outcome and an associated monetary value amount generated; andtransmit the optimal treatment plans to be presented on a computingdevice.